A group of interns in a lecture theatre
Graduate Access Programme participants attending a lecture
(Image credit: Phil Brooks)

UNIQ+ projects

Projects available for entry in 2023

As part of your UNIQ+ Research Internship, you will be working on a project under the supervision of academic staff from our community of world-leading researchers.

Places for UNIQ+ internships will be distributed across a wide range of subject areas with around 120 places available in total. The application form will ask you to outline your preferred field of study and areas of research, and select at least one and up to three preferred projects that you are interested in working on. It is expected that the projects you select would usually be in a similar subject area.

The next sections of this page provide details of the projects available this year, categorised by research area. Many of our projects are open to those studying undergraduate degrees in a broad range of subjects, however you should read the project descriptions carefully and check to see if a project has any specific entry requirements before applying.

Full instructions for completing the application form can be found in our Application Guide.

How are places distributed?

If you are successful, we will try to match your interests to available projects and supervisors. We will also match you to funding on the basis of your interests, project/supervisor availability, and closest match with the eligibility criteria, in order to maximise the number of places we can offer.

Please note that we will not always be able to meet your preferences for a project/supervisor, but we will try our best to do this wherever possible. Only projects that are matched to successful applicants will run this year.

Potential ten-week internships

Some projects may be funded by an external partner for an extended duration of ten weeks with an associated scholarship stipend of £4,200 (this will be indicated in the project description where applicable). Ten weeks projects will run from Monday 3 July to Friday 8 September. The other benefits are the same as those for UNIQ+.

Economic and Social Research Council (ESRC)-funded UNIQ+ Research Internships

We intend to offer up to two ESRC-funded UNIQ+ Research Internships to individuals who meet the eligibility criteria and apply for the projects in the social sciences that are eligible for ESRC funding (this will be indicated in the project description where applicable).

The benefits of an ESRC placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance).

Wellcome Biomedical Vacation Scholarships

We intend to offer around six Wellcome-funded UNIQ+ placements to individuals who meet the eligibility criteria and apply for the projects in the medical sciences that are eligible for Wellcome funding (this will be indicated in the project description where applicable).

The benefits of a Wellcome Biomedical Vacation Scholarship placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance).

Confirming externally-funding places

If you are successful with your application to one of these projects and your place is funded by an external sponsor, your offer will make this clear and provide all the contractual details, including how you will be paid.

Please note that there may be some amendments to the published information for internships funded by external sponsors in line with specific agreements with these funders. These amendments will be published as soon as they are available.

Projects in Humanities and Social Sciences are offered by the following departments:

Archaeology

Archaeology 01 
Tracing elusive female scholars in Chinese archaeology

Supervisor

Dr Anke Hein

Description

The year 2021 has been declared the centenary anniversary of Chinese archaeology, leading to a flood of talks, conferences, and publications on the history of the discipline. The stories told there, however, and the scholars appearing at related events, are nearly exclusively male, even though the number of female students in the field has increased dramatically over the years. This project aims to trace the elusive female scholars in Chinese archaeology, compiling information on practitioners at museums, universities, and cultural heritage institutions, as well as data on student admission over the years, investigating trends in the admission of female students to archaeology programs but also trying to find out where graduates end up. You will create questionnaires to learn about the studying and working conditions for women in the field of Chinese archaeology both in China and abroad.

Project outcomes

You will be building a database of female scholars in the field of Chinese archaeology. You will also create several questionnaires aimed at investigating the situation of women in the field of Chinese archaeology. You will then produce a preliminary report on the findings of this investigation.

Entry requirements

The main thing needed is curiosity and basic competency with computers and internet searches. No prior knowledge on Chinese archaeology is required (though of course welcome if it happens to be there). Chinese-language skills are likewise not required but interest in engaging with unfamiliar terms, names, and concepts is needed. Familiarity with excel or databases would be useful but is not required and training is available. Some familiarity with principles in anthropology, archaeology, and/or history would be useful but likewise not required and there will be introductory lectures on the topic.

Archaeology 02 
Dating archaeological sites in NW Africa using volcanic ash layers

Supervisor

Professor Victoria Smith

Description

Many Middle Palaeolithic archaeological sites have poor chronologies and thus, it is not possible to directly compare technologies and changes between sites. We have sediment samples from a suite of cave sites in Morocco that likely span the last 200,000 years. These caves are rich in archaeological remains and dating these records is crucial to understand human evolution, technological change, and migration in this area. Fortunately, ash from volcanoes in the Canary Islands and Azores has been found in some sequences across the region. Eruptions from these volcanoes are well dated and locating their ash in the cave sediments provides an age and can be used to correlate the archaeological records. The aim of this project is to find volcanic ash layers in the sedimentary records using density separating techniques in the laboratory, and link the layers to dated eruptions by analysing the composition of the volcanic glass shards and comparing these data to our glass composition database for the volcanic islands. 

Project outcomes

Proficient in the laboratory skills to identify volcanic ash layers that are not visible in sediments, and understanding characterisation and correlation to known and dated volcanic eruptions.  

Entry requirements

Knowledge of archaeology, geography and/or earth science.

Archaeology 03 
Understanding whether the shape of volcanic ash particles affects dispersal 

Supervisor

Professor Victoria Smith

Description

Large volcanic eruptions disperse volcanic ash (tephra) hundreds to thousands of kilometres from source. At these locations that are distal to the volcanic vent, the deposits form layers often that are millimetres to decimetres in thickness but occasionally they are so dilute and fine that they cannot be identified by eye. Recent studies have suggested that tephra thickness in the distal environment is dependent on particle shape. We have an impressive volcanic record from a lake in Japan, Lake Suigetsu, that records more than 50 explosive eruptions as both visible and non visible (termed cryptotephra) layers. This project will involve imaging volcanic glass shards from various tephra layers that are from a couple of volcanoes in Japan. The aim is to establish whether there is a relationship between glass particle shape and tephra thickness. e shape of volcanic ash particles affects dispersal 

Project outcomes

Experience using and taking images on a scanning electron microscope, and processing the images in MatLab or Python. The data will be able to assess what particle shape characteristics control dispersal distance.  

Entry requirements

General knowledge of earth science and/or geography

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Classics

Classics 01 
Documenting Cultural Heritage through the Manar al-Athar Digital Archive  

Supervisor

Dr Miranda Williams

Description

The Manar al-Athar Digital Archive provides images of archaeological sites and historic buildings from the Middle East and North Africa for teaching, research and heritage work. This project aims to provide interns with the opportunity for both digital humanities skills development and traditional academic research. In the first part of the project, interns will gain experience in the use of digital collections, both for research and as a subject of research, through the preparation of a set of photographic data relating to an assigned archaeological site in the Middle East or North Africa. In the second part, the intern will develop his/her research skills by mapping the research landscape relating to his/her site, and identifying key gaps, through the production of an annotated bibliography (expanding on preliminary bibliography which will be provided); and by the presentation of a topic identified through this research to a non-specialist audience in the form of an ArchGIS StoryMap, an interactive map combining text and multimedia. Full training in the software to be used during the internship (Adobe Bridge and Photoshop, ArchGIS StoryMaps) and in Manar al-Athar’s digital asset management system, ResourceSpace, will be provided.

Project outcomes

The project will result in the preparation of a complete set of photographic data relating to an archaeological site or sites and its uploading to the Manar al-Athar Photo-Archive. In so doing, interns will not only gain a detailed knowledge of a heritage site in the Middle East or North Africa, but will also develop digital skills. The preparation of a photo-collection will involve library-based research to ensure the accurate labelling and curation of photographs. This research will result in two further outcomes:

  1. the development of an annotated bibliography relating to the designated site; and
  2. the production an ArchGIS StoryMap on a topic identified through the preparation of the photo-collection and production of the annotated bibliography.

During the project, the intern will be an integral part of the Manar al-Athar project team. They will gain experience undertaking traditional, library-based, research, as well as in the technical skills necessary to work with digital photographs and related metadata. In preparing the ArchGIS StoryMap, the intern will have the opportunity to practice communicating academic research to a non-specialist audience.

Entry requirements

There are no specific entry requirements for this project – all necessary training will be provided. However, an interest in the archaeology, history, and/or cultural heritage of the Middle East and/or North Africa is desirable. The project may therefore be of particular interest to students undertaking degrees in Archaeology, History, Classics, Middle Eastern Studies, and Cultural Heritage Studies.

Classics 02 
The History of Oxford Classics 

Supervisor

Professor Tim Rood

Description

You will support a project investigating the history of Classics in Oxford. One of the goals of this project is to prepare a consolidated (digital) archive for the Oxford Classics Faculty. Relevant materials on the study of Classics at Oxford since the establishment of the Literae Humaniores degree in the early 1800s are currently scattered across a variety of colleges (especially for individual scholars) and private collections as well as a number of different holdings in the Bodleian and the University Archive. You will help in the compilation of a preliminary list of potential sources of material with their current locations and custodians; we would expect this to be accomplished for the most part through digital searches and emails to archivists. You will then select one archival source and investigate its significance for the study of the history of Classics in Oxford.

Project outcomes

You will gain experience undertaking traditional, archive-based, research, as well as training in key bibliographical and research methodologies. You will identify further sources for inclusion in the project database and write a short article or report on either a collection or specific source which you have identified. The article will be considered for publication on the faculty website or in its newsletter.

Entry requirements

There are no specific entry requirements. An interest in the culture of the ancient Mediterranean is desirable. The project may be of particular interest if you are undertaking a degree in archaeology, classics, history or education, although those studying other subjects are welcome to apply.

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Economics

Economics 01 
Global priorities research (economics) 

Supervisor

Professor Julian Jamison

Description

The field of global priorities research investigates academic questions that arise in response to the question, ‘What should we do with a given amount of limited resources if our aim is to do the most good?’ The researcher will work with Professor Julian Jamison to develop a project in economics that is suitably aligned to the Global Priorities Institute’s research agenda. The Global Priorities Institute website provides more information about this research agenda. The output of your research will be an original research paper, or a component of a research paper.

Project outcomes

You will conduct preliminary research to familiarize yourself with the field of global priorities research and to formulate a specific research question within the field. You will then use economic tools to work towards an answer to this question. The culmination of this work will be a research paper or a component of a research paper.

Entry requirements

There are no specific entry requirements.

Funding information

This internship may be funded by the Economic and Social Research Council (ESRC). The benefits of an ESRC placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about ESRC-funded placements.

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Education

Education 01 
The Learning for Families through Technology project 

Supervisor

Dr Pinar Kolancali

Description

The Learning for Families through Technology (LiFT) project is a collaboration between Ferrero International, Gameloft and the Department of Education, Oxford. LiFT is aimed at developing and evaluating the educational potential of app-based interactive activities for children and families. This project has developed and implemented a rigorous research agenda that combines insights from three streams: Vocabulary and Creativity, Joint Media Engagement, and Learning Analytics, to investigate language learning (for native and non-native speakers), creativity development, and parent-child interaction outcomes arising from the use of technology. For Creativity, we are reviewing a sample of commercially available apps that claim to support primary school children’s creativity to assess their quality (based on research evidence) and whether features of apps such as review ratings or cost relate to their quality. We will be conducting a randomized controlled trial (RCT) study to assess if certain features of apps support children's creativity. For Vocabulary, we have developed a digital vocabulary game to teach children the meaning of homonyms (when one word has several meanings, such as bat – an animal or a piece of sports equipment). The app is being evaluated through an RCT in schools and through learning analytics from app users. For Joint Media Engagement, we are working on a large-scale international survey of the Applaydu app users to establish whether and how children and adults use technology together at home, exploring the potential predictors of joint media engagement.

Project outcomes

The Learning for Families through Technology (LiFT) website provides an details of the overall aims of the project.

Entry requirements

An undergraduate/master's degree in Education or Psychology

Funding information

This internship may be funded by the Economic and Social Research Council (ESRC). The benefits of an ESRC placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about ESRC-funded placements.

Education 02 
Future ETC hub (Future of Education and Training for the Climate) 

Supervisor

Dr Steve Puttick

Description

Education and training have an essential role to play in the global mitigation of and adaptation to climate change. The Future ETC (Future of Education and Training for the Climate) Hub is mapping out relevant expertise at Oxford, making the breadth and depth of this research visible for the first time through a vibrant online presence, and bringing together an expansive network in Oxford and a strategic group of international, interdisciplinary collaborators to generate new research insights into education and training for the climate. This internship project will provide an opportunity to contribute to the development of the hub across stakeholder mapping, website content development, and communications. Working with the PI and wider project team (particularly the RO and RA), interns will gain a greater insight into education and training for the climate at Oxford and beyond while developing their networking and communication skills.

Project outcomes

Working on the Future ETC Hub will be directed particularly towards supporting the hub’s work on mapping the research on Education and Training for the Climate (ETC) that is happening at Oxford and beyond. The main outcome will be to help shape the hub’s web presence. Depending on the skills and interests of the intern, there is scope to contribute more widely across the breadth of the project including gaining experience of report and bid writing.

Entry requirements

We look for applicants interested in education and training for the climate, rather than specific qualifications or disciplinary expertise. The interdisciplinary nature of the hub means that we are open to contributions for all areas.

Funding information

This internship may be funded by the Economic and Social Research Council (ESRC). The benefits of an ESRC placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about ESRC-funded placements.

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Geography

Geography 01 
Deep learning for predicting streamflow variability at seasonal time scales 

Supervisor

Dr Louise Slater and Dr Simon Moulds

Description

The ability to predict streamflow variability up to a year ahead is of enormous value to water managers. Current operational forecasts of streamflow variability use physics-based models which are highly sensitive to the underlying forecasts of weather and climate. Recent research has shown that long short-term memory (LSTM) neural networks outperform traditional conceptual and physics-based hydrological models when they are forced with observed climate inputs over historical time periods. However, their performance using seasonal climate forecasts, which often contain significant biases and errors, is currently under explored. This project will test the ability of LSTM models to predict seasonal streamflow variability up to a year ahead. Potential study regions include the US or the UK. The modelling will be carried out in Python using the Neural Hydrology package, so familiarity with Python is essential. Previous experience working with large datasets would also be an advantage, as an important part of the project will be “feature engineering” to identify and extract input variables to improve the quality of streamflow predictions. Experience with shell scripting on Unix-like systems would also be beneficial, as the student will use the Oxford Advanced Research Computing service to train the LSTM models.

Project outcomes

Project outcomes: the student will gain experience and expertise in the following areas: predictive modelling (time series analysis); feature engineering; big data analysis; understanding of seasonal climate forecasts; evaluation of seasonal streamflow forecasts; understanding how to produce publication-quality scientific figures; scientific manuscript writing (if time). The student will be included as a co-author on any publication(s) resulting from their research.

Entry requirements

Essential: fluency in Python; an interest in climate and hydrology. Desirable: previous experience working with large datasets; experience with shell scripting.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

This internship may also be funded by the Economic and Social Research Council (ESRC). The benefits of an ESRC placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about ESRC-funded placements.

Geography 02 
Interpreting 3-dimensional digital models of cities 

Supervisor

Professor Gillian Rose

Description

This qualitative project is part of a larger piece of work which will investigate different examples of how cities are being visualised using different kinds of digital software, from movie VFX to augmented reality apps. My background is cultural geography so I am interested in the meanings and affects of those different visualisations, and in particular how they picture the human inhabitants of urban spaces (though I would also be interested in learning from you if you have more technical expertise in this area). The project will investigate visual and textual representations in 3D city models and will have 4 stages: 1 identifying the main creators/vendors of such models; 2 downloading their website content; 3 designing and operationalising content and discourse analyses of that content; 4 writing a short report on your findings.

Project outcomes

The project will have three outcomes:

  1. a database of the most significant online sites for obtaining urban 3D models;
  2. an archive of their websites; and
  3. a report analysing their representation of city space and city inhabitants.

Entry requirements

A background in cultural geography, urban studies, cultural studies (including film or game studies) or architectural studies would be helpful but not necessary. I will provide a small number of initial readings to orient you to the approach of the project and to the methods of content analysis and discourse analysis.

Funding information

This internship may be funded by the Economic and Social Research Council (ESRC). The benefits of an ESRC placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about ESRC-funded placements.

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History

History 01 
Oral History and the lived experience of epilepsy in low to middle income countries 

Supervisor

Dr Sloan Mahone

Description

Our project brings together a dedicated multi-disciplinary team of medical/public health professionals and historians to significantly improve the quality of life of people with epilepsy in resource poor settings. Epilepsy is a highly stigmatised condition with a long history of social exclusion and discrimination. We work in Kenya, South Africa, Zimbabwe, Brazil and India with local collaborators in order to implement 'embedded oral history projects' that inform public health initiatives. We would welcome interns who have an interest in oral history, the history of medicine, the history of psychiatry and mental health, and medical humanities.

Project outcomes

We are flexible with regard to outcomes of the internship but envision that you will work in public engagement and outreach, research analysis with oral history content and literature reviews, website and communications development, research communications, the development of oral history training guides, and coordination with our overseas collaborators.

Entry requirements

We would welcome interns with interests in health research and public health interventions, oral history, and health communication. An interest in writing and public outreach is essential but we are prepared to offer mentorship in this area. An interest in or knowledge of epilepsy or neurological conditions is useful but not essential.

History 02 
International banking connections and international commerce 1870-1980 

Supervisor

Professor Catherine Schenk

Description

International bank connections are a fundamental building block of globalisation. This project will help to uncover how banks made these connections in the 19th and 20th centuries. The research will involve going to archives to read original documents, using data collected from other sources and/or collecting new evidence to understand how individual banks made links across long distances to support the needs of their customers. Regions might include South America or European financial centres with links to London. The researcher will be part of a team of established academics in the ERC-Funded project GloCoBank (Global Correspondent Banking) 1870-2000 and will take part in team meetings to hear about other parts of the project. The project aims to introduce you to a range of historical methods, including how to use primary documentary sources and data management.

Project outcomes

Interns will gain experience using primary sources and data management, including data cleaning and organisation. The interns will also learn about the history of international banking, interpreting bank data and the history of financial globalisation. Depending on the intern’s skills, the outcome may be a new dataset, a visualisation of an existing dataset (e.g. using Tableau), a written case study of the links of a particular bank over time or a combination of these outcomes. Interns will also contribute blogs on their research progress. All outputs will be shared with the public on the GloCoBank website.

Entry requirements

We welcome applicants with an interest in history of global economic relations since 1870. Experience using Excel would be useful but not required.

History 03 
Childhood and Inequality in Modern Britain 

Supervisor

Dr Sian Pooley

Description

Most of what we know about the past tells the story of adult actions, beliefs, and experiences. Oxford University's History Faculty has hosted the UK's only Centre for the History of Childhood since 2003. Over the last twenty years researchers have sought to find sources to include the experiences and voices of the young in the histories we write. 'Childhood and Inequality in Modern Britain' aims to expand this research agenda. Your research will focus on the experiences and perspectives of people who were marginalised not only because of their youth, but also because of their race, religion, class, gender, disability, or family circumstances. As an intern on this project, you will explore a specific archive to find out what these sources tell us about the lives of some of the most marginalised and disadvantaged children in nineteenth- and twentieth-century Britain. You will work independently to develop your own research specialism as part of a team of historians.

Project outcomes

At the end of the project, you will have a strong understanding of the principles, methods, and practices of historical research. You will write a report on the findings of your archival research which will guide future research on the history of childhood and inequality in modern Britain. You will also give a presentation to a seminar organised by the Centre for the History of Childhood and publish a blog post on the Centre's website to share your research findings more widely. You will also experience what it is like to work collaboratively within a research community who will support you to develop your own research interests and skills.

Entry requirements

Experience in history from your undergraduate degree is desirable but not essential. We would also welcome other humanities or qualitative social science applicants with an interest in modern childhood and inequality.

History 04 
The Mighty Dead: Royalism and Popular Culture during the English Civil War 

Supervisor

Dr Sarah Mortimer

Description

The noble deaths of English aristocrats become major news stories in the 1640s, sensationalised in the popular press as well as memorialised in stone and in literature. This project will explore how these moments of drama and tragedy became a crucial part of royalist popular culture, reflecting and shaping contemporary perceptions of honour, masculinity and virtue. Interns will use printed pamphlets and newsbooks alongside portraiture and battlefield material culture, accessible in the Bodleian library or in Oxford. The interns will benefit from the co-supervision of Dr David Scott of the History of Parliament Trust (HoPT), and gain insight into the research culture of the HoPT.

Project outcomes

A blog from each intern, to be displayed on the history faculty website and on the History of Parliament website. The research they produce will also contribute to the biographies being written by the History of Parliament House of Lords 1640-1660 section.

Entry requirements

History or another relevant humanities degree; some knowledge of early modern history and experience of working with early modern sources would be an advantage.

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Interdisciplinary humanities

Humanities 01 
Digital Voltaire 

Supervisor

Professor Nicholas Cronk

Description

The ambition of the Digital Voltaire project is to create a single-author digital resource and model of its kind. The vision is for a critical edition conceived as a digital object, exploring the potential of this new mode of publication and shaping the next generation of textual editions. The high quality of scholarship within the Complete works of Voltaire (205 vols, 1968-2022) is the starting point. On this project you will support the work of Digital Voltaire to ‘deconstruct’ volumes into their constituent parts (textual, bibliographical, explanatory), enabling them to be accessed, searched and researched in new ways. Supplementary materials (e.g. texts, images, biography, library catalogue, manuscript catalogue) will form a parallel online resource, The Voltaire Studio. As an intern you might assist the following activities: transcribing texts of newly digitised manuscripts, learning about TEI tagging, revising the print edition, cataloguing recently discovered Voltaire manuscripts, user testing of early prototypes of the Digital Voltaire interface, or updating the corpus of Voltaire letters. Projects will be tailored to suit interns’ particular interests and skills and interns will create a blog to report on their work.

Project outcomes

A discrete part of Digital Voltaire resource which the intern could sign and a blog post.

Entry requirements

We can be flexible; knowledge of French useful; also previous study of literature and/or history helpful

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Linguistics, Philology and Phonetics

Linguistics 01 
The meaning of ‘meaningless’ pronouns: Middle English to Modern English  

Supervisor

Dr Louise Mycock

Description

The ProTag Construction is an excellent example of how language conveys more than literal meaning. A ProTag is an apparently superfluous pronoun added to a clause, as in “That’s good quality, that” or “I’m a hard worker, me”. In each case, the clause would be fully interpretable if the final pronoun were omitted. This project aims to answer the question: Why would any speaker of English use a ProTag? ProTags are not a recent innovation: this construction has been part of the English language for centuries. Previous research has found evidence of ProTag use in the works of Jonson, Marlowe, and Shakespeare. Working with data from the 13th century to the 20th century, you will review examples of ProTag use, identify their functions according to a classification system which is in development, and construct profiles of the characters who use ProTags to determine what factors might influence their occurrence.

Project outcomes

You will receive training in corpus linguistics methods, gain skills in reading scientific papers and undertaking qualitative analysis of linguistic data, acquire experience of working with data from historical varieties of English, contribute to the development of a system for classifying ProTags according to their functions, and compile character profiles that will provide new insights into who uses ProTags in literature spanning from Middle English to Modern English. You will report and discuss your findings at regular meetings during the project, before writing and presenting a final report outlining and explaining the findings of your research. This report will also be the basis for an online article. The findings of this project are expected to contribute to a peer-reviewed publication of which you will be a co-author.

Entry requirements

Knowledge of Middle English and Early Modern English from your undergraduate degree is essential. You should have an interest in analysing language, but previous experience of linguistic analysis is not required. Familiarity with Excel would be useful but is not essential and training is available.

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Oxford Internet Institute

Internet Institute 01 
Teaching reinforcement learning algorithms to cooperate by watching humans 

Supervisor

Dr Luc Rocher

Description

Reinforcement Learning (RL) is a highly successful field of research, with superhuman performance in games like Chess and Go as well as scientific advancements. In our team, we are interested in the development of multi-agent RL environments, where algorithms learn to cooperate and collaborate with humans. This has potential to inform the future of AIs such as true self-driving cars, robot-empowered doctors, or personal assistants. Reinforcement Learning generally requires large amounts of training data, which makes working with humans a challenge, since humans are unwilling and unable to run millions of repetitions of a task to teach an AI. This project explores methods to introduce human experience to train RL agents. Of particular interest are methods which minimise the need for human training experience. The project will test how to train a variety of AI agents with different methods of providing access to human data. The resulting agents will then be compared in online experiments to test the effectiveness of the different ways of teaching the agents about people.

Project outcomes

The project compares the effectiveness of various approaches to introducing human data into cooperative multi-agent reinforcement learning settings. We are interested in the ability of trained agents to cooperate with human partners as well as assessing whether learning from human data can be simulated or abstracted from a minimal amount of actual experience.

Entry requirements

Experience with machine learning and artificial intelligence are desirable. Previous experience with Python and web programming will be essential.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Internet Institute 02 
Human diversity in cooperative reinforcement learning 

Supervisor

Dr Luc Rocher

Description

The history of artificial intelligence is filled with examples of models that failed to incorporate human diversity. Researchers have for instance pointed out the failures of facial recognition systems from tech companies that dramatically misclassify non-white faces. Our team studies the risks and benefits of reinforcement learning (RL), a class of algorithms that learn by collecting experiences from their environment and progressively preferring options which lead to better outcomes, with famous successes such as AlphaGo. This project investigates how to better incorporate human diversity and experience into RL algorithms, when performing a shared task with a human partner, as is the case with some doctor-AI teams, or with tutoring agents. Our aim is to design algorithms that can successfully cooperate with people whose behaviours are least similar to those represented in training.

Project outcomes

This project will provide empirical evidence regarding the potential for bias in agents trained through reinforcement learning. It will involve programming and training an AI as well as conducting online experiments with the trained agent.

Entry requirements

Experience with machine learning and artificial intelligence are desirable. Some previous experience with Python and web programming will be essential.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Internet Institute 03 
Adversarial learning for Multi-Agent Reinforcement learning 

Supervisor

Dr Luc Rocher

Description

Recently, adversarial techniques have been proposed against a range of machine learning methods, allowing a malicious adversary to exploit small vulnerabilities. This can involve tricking a facial recognition algorithm into not seeing a face by changing a few pixels or adding words to a message to thwart spam detection. Including adversarial techniques when training AI can help identify and remove weaknesses, in order to improve the overall robustness of machine learning models. In this project, we are interested in the development of adversarial techniques against multi-agent reinforcement learning, a class of algorithms that can be trained to collaborate with human actors to play videogames, discover new proofs, or drive cars. This is a practical project combining machine learning and programming that aims to test whether modern techniques such as ‘counterfactual regret’ can help learn the flaws of these algorithms in practice.

Project outcomes

The project will attempt to apply adversarial techniques to multi-agent reinforcement learning. We are interested in using adversarial learning to discover useful patterns in the algorithmic weaknesses, in order to guide the design of safer algorithms.

Entry requirements

Experience with machine learning and artificial intelligence are desirable. Some previous experience with Python programming will be essential.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Internet Institute 04 
Detecting Anomalies in Censorship Circumvention Data  

Supervisor

Dr Joss Wright

Description

You will contribute to an existing tool which detects ongoing per-country anomalies in the daily usage metrics of the Tor anonymous communication network. The current tracker’s approach identifies contiguous anomalous periods, rather than daily spikes or drops, and allows anomalies to be ranked according to deviation from expected behaviour. The tool is currently used by many individuals, academics, and NGOs as an early warning system for potential censorship events. This project, in particular, will look to better the existing tracker by either; (a) improving the anomaly detection algorithm, (b) adding other data sources which may allow more specific identification of likely censorship events, or; (c) expanding the tool into an R Shiny App to enable more engaging user experience interactions and displaying basic inferential statistics. This project will develop your coding skills in R and your ability to work with large data sets.

Project outcomes

'You will contribute to the development of an anomaly detector using data science methods. Work will primarily be done using R. You will also have the option to produce a blog to promote your contributions and work on the project.

Entry requirements

Knowledge of coding in R is required. An demonstrable interest in censorship related topics would be beneficial.

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Philosophy

Philosophy 01 
Determining the 'zero level' for lifetime welfare  

Supervisor

Dr Andreas Mogensen

Description

We may believe that there can be some lives so bad - so overcome with terrible suffering - that it would have been better had they never been lived. Conversely, many people regard their own lives as well worth living and are glad to have been born. These intuitions give rise to the idea of a ‘zero level’ for lifetime welfare:  a level of welfare above which a life is worth living, because the good outweighs the bad, and below which it is not, because the bad outweighs the good. How, if at all, can we make the idea of a zero level for lifetime welfare more rigorous and precise? Can it be defined so as to be neutral with respect to the correct theory of welfare? How, if at all, can it be operationalised so as to allow us to determine whether a given individual - be they a human being or a non-human animal - is above or below the zero level for welfare? 

Project outcomes

You will conduct research designed to help answer these questions or more targeted sub-questions relevant to the topic. Your research will primarily consist of surveying and evaluating existing literature related to the zero level for lifetime welfare, formulating your own views in response and in discussion with your supervisor, and writing up your conclusions in the form of a report. Depending on the research direction you pursue, you may end up engaging not only with the existing philosophical literature in ethical theory, but also relevant literature on the measurement of human subjective well-being in psychology and economics and/or animal welfare science. During the first weeks of the project, you will focus on developing clearly defined goals for your project and a plan for fulfilling those goals, after which you’ll focus on executing the project design, with an eye to producing a report or essay as the final output of the project.

Entry requirements

Philosophy or another relevant degree subject. A background in moral philosophy is desirable, but not required.

Philosophy 02 
Decision-making under deep uncertainty

Supervisor

Dr David Thorstad

Description

Many of our most challenging decisions take place under conditions of especially high, or `deep' uncertainty. However, leading theories of rational decision making are often inapplicable or unhelpful under deep uncertainty. As a result, there have been increasing efforts to develop new procedures for decision making under deep uncertainty. This project centers around three questions. First, what is the nature of deep uncertainty? Second, to what extent are new approaches needed for decision making under deep uncertainty? And third, what are some of the most promising theories of decision making under deep uncertainty? 

Project outcomes

You will conduct research designed to help answer these questions or more targeted sub-questions relevant to the topic. Your research will primarily consist of surveying and evaluating existing literature on decision making under deep uncertainty, formulating your own views in response and in discussion with your supervisor, and writing up your conclusions in the form of a report. Depending on the research direction you pursue, you may engage with the philosophical and economic literatures in decision theory, or with the emerging trans-disciplinary literature on decision making under deep uncertainty in risk analysis, decision analysis, and allied fields. 

Entry requirements

Background in philosophy preferred. It is especially helpful to have some knowledge or experience in social or political philosophy (broadly construed to include ethics and moral philosophy), decision theory (or other areas of formal philosophy), philosophy of mind, or epistemology, although applicants should not be discouraged from applying if their interests lie elsewhere. 

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Politics and International Relations

Politics 01 
‘Europe’ Through the Eyes of Ukrainian Youth  

Supervisor

Dr Marnie Howlett

Description

Despite historical discussions about Ukraine’s geopolitical place between Europe and Russia, and growing narratives around Ukraine's potential membership within the EU since Russia’s February 2022 invasion, how Europe is perceived by individuals living in the country have routinely been under explored. The perspectives of young people in particular have been significantly overlooked, yet, are currently perhaps more important than ever before as they are the future leaders of Ukraine and will be responsible for its domestic and foreign policy objectives. This project therefore asks: how and why do youth in Ukraine view and understand Europe and the EU? What are their current relations with Europe, including points of convergence and divergence? And finally, how do they see Ukraine’s future relations with Europe? This project seeks to uncover how young Ukrainians (aged 18-29) see and understand Europe. While uncovering young Ukrainians' views about the future of their state, this project also helps us to better understand the symbolism of the EU for Ukrainians following the war.

Project outcomes

This project utilises focus groups, along with survey and social media data, to understand Ukrainian youths' perceptions of the EU and Europe, including how they relate to Europe and where they see future relations developing between Ukraine and Europe into the future. You will be involved in a mix of coding and analysing focus group transcripts and social media data, as well as running regressions and/or descriptive statistics. There also may be scope to undertake some qualitative data collection and analysis (particularly content and discourse analysis) to assist with ongoing work. One aim of the internship is also a co-authored paper with the supervisor and the other interns working on the project.

Entry requirements

You should have experience in a relevant social science discipline (eg economics, political science, sociology, anthropology) from your undergraduate degree. Some background in qualitative and quantitative research methods (and use of NVivo, STATA, SPSS and/or R) would be highly desirable but not essential.

Funding information

This internship may be funded by the Economic and Social Research Council (ESRC). The benefits of an ESRC placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about ESRC-funded placements.

Politics 02 
Non-intervention and the Global South  

Supervisor

Dr Patrick Quinton-Brown

Description

What is the meaning of non-intervention in the thought and practice of the Global South? How have institutionalised practices of non-intervention evolved in and through decolonisation? Recently the dominant way of thinking about these issues has been through the lens of the Responsibility to Protect (RtoP). Yet it seems doubtful that this framework is still capable of posing the right questions and generating the right sorts of answers. The broader project is concerned with re-interpreting the sovereignty/intervention debate and expanding its scope beyond liberal-solidarist or cosmopolitan duties. Your work would help to re-contextualise and critically evaluate the study of intervention by gathering and analysing data relating to a particular case study in Southern Africa.

Project outcomes

You will produce a literature review as well as a short report or presentation drawing from official documents and archival materials.

Entry requirements

You should have experience in a relevant discipline and an interest in the theory and history of decolonisation in international society.

Funding information

This internship may be funded by the Economic and Social Research Council (ESRC). The benefits of an ESRC placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about ESRC-funded placements.

Politics 03 
Intergenerational cycle of violence: the effects of war on intimate partner violence  

Supervisor

Dr Ria Ivandic

Description

Conflict has long-lasting effects on its populations. It is suggested that trauma can be a trigger for unstable mental health and violent behaviour. Yet, the extent to which events trigger domestic violence and how they may lead to intergenerational cycles of violence remains less understood. Post-traumatic stress disorder (PTSD) is a mental and behavioural disorder that can develop due to exposure to a traumatic event, such as warfare, and PTSD is, in turn, positively associated with aggression perpetration (Shorey et al, 2012). In this research, I will examine how the heterogeneous exposure to violence during the most recent largest conflict in Europe, the Croatian War of Independence and Bosnian War, affected patterns of intimate partner violence decades after. This research will consist of collecting and cleaning administrative data, and conducting quantitative analysis.

Project outcomes

The applicant would help gather data on intimate partner violence and conduct descriptive analysis of its trends, that will be part of a research article.

Entry requirements

There are no specific entry requirements.

Funding information

This internship may be funded by the Economic and Social Research Council (ESRC). The benefits of an ESRC placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about ESRC-funded placements.

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Social Policy and Intervention

Social Policy 01 
Quantitative methods to examine inequality in educational achievement using large-scale assessment data  

Supervisor

Mr Mobarak Hussain

Description

The aim of the project is to develop quantitative methods skills using STATA or R to examine educational inequalities by social origins across countries using large-scale assessment data. We will specifically use the Programme for International Student Assessment (PISA) dataset, which contains data on the educational achievement of 15-year-olds from OECD and non-OECD countries. The dataset has also information about students' family backgrounds, and school and teacher characteristics. We will demonstrate how to use this dataset to understand educational inequalities across countries.

Project outcomes

The project outcome will be to:

  1. Develop skills to use PISA data using STATA/R; and
  2. Write a short report analysing the data to demonstrate educational inequalities by social origins.

Entry requirements

This course will be suitable for students with a basic background in social statistics. However, depending on the skill level, the course materials can be adjusted, which means students with no background in social statistics can also attend the course. Students are not expected to know how to use STATA or R although having some experience in using statistical software may be beneficial.

Funding information

This internship may be funded by the Economic and Social Research Council (ESRC). The benefits of an ESRC placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about ESRC-funded placements.

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Sociology

Sociology 01 
Refugees Welcome? Historicizing U.S. Resettlement and Asylum Policy  

Supervisor

Dr Molly Fee

Description

This project will examine the evolution of humanitarian policy responses to refugees and asylum seekers in the United States over the past four decades (1980 to present). Research will use a comparative lens to investigate how refugee resettlement and asylum policy developed into two distinct approaches, when and why periods of relative openness have been followed by others of relative restrictiveness, as well as why different groups seeking refuge have received differential treatment. This project will focus on how the priorities of presidential administrations and congressional actors have shaped contemporary refugee protections in the United States.

Project outcomes

At the end of this internship, you will have gained a deeper understanding of the issues and debates that shape government responses to refugee protection. You will develop a working knowledge of refugee and asylum policy in the United States through online archival research and the analysis of policy documents, the Congressional Record, and relevant academic literature on forced migration. You will produce a timeline of key developments in policy and law, graphs and charts related to refugee admissions, and a repository of primary sources.

Entry requirements

There are no specific entry requirements.

Funding information

This internship may be funded by the Economic and Social Research Council (ESRC). The benefits of an ESRC placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about ESRC-funded placements.

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Projects in Mathematical and Physical Sciences are offered by the following departments:

Chemistry

Chemistry 01 
Synthesis of aryl hydrocarbon receptor targeted degraders 

Supervisor

Professor Angela Russell

Description

The project forms part of an ongoing programme of work in our lab to develop modulators of utrophin for the treatment of Duchenne muscular dystrophy. In 2018 in a clinical trial of ezutromid (first-in-class utrophin modulator developed as a result of our work) we found that the drug gave an increase in utrophin and provided functional benefit in patients, but these positive effects were unfortunately not sustained. In subsequent work we have been able to rationalise the lack of sustained efficacy and uncovered the molecular target and mechanism of action of ezutromid. Ezutromid acts as an antagonist of the arylhydrocarbon receptor (AhR) in muscle to exert its effects (Angew Chem, 2020). We have since been exploring alternative approaches to increase the duration of action of AhR antagonism. One of these approaches is to create a bifunctional molecule to recognise, antagonise and selectively proteolytically degrade AhR in the cytosol (using a PROteolysis-TArgeted Chimera (PROTAC)-type approach). The candidate will design and synthesize 1-2 examples of these PROTACs targeting AhR and, time permitting, have an opportunity to test them in muscle cells to measure AhR antagonism, degradation and utrophin upregulation.

Project outcomes

The project will form a significant part of our ongoing collective work to develop AhR targeting ligands to upregulate utrophin for Duchenne muscular dystrophy. The expected outputs are:

  1. Synthesis of up to 2 AhR PROTACS ready for testing; and
  2. Data confirming effects of the PROTACS on AhR antagonism, degradation and utrophin upregulation in muscle cells (either as part of the project, time permitting or tested immediately afterwards).

The work is expected to form a substantial part of a publication describing the feasibility of a PROTAC approach in utrophin modulation.

Entry requirements

The candidate should be studying for a degree in chemistry or a degree where chemistry forms a substantial component of the degree.

Chemistry 02 
Inorganic Chemistry for Future Manufacturing 

Supervisor

Professor Simon Aldridge

Description

Three research projects are available to students with an interest in inorganic chemistry across different length scales. Students will learn skills associated with the synthesis of molecules, nanoparticles or solids, and gain critical experience handling inorganic compounds. The projects will focus on the synthesis of novel compounds with potential applications in fields such as catalysis, energy storage and chemical synthesis. Applicants can select projects in one of the areas prior to starting in the laboratory and will be assigned a supervisor with the relevant expertise.

Project outcomes

Students will learn how to:

  • safely handle reactive compounds including materials that are air- and moisture-sensitive;
  • analyse chemical compounds using state-of-the-art techniques (including, for example, nuclear magnetic resonance (NMR) spectroscopy, X-ray diffraction, mass-spectrometry, electrochemical methods);
  • interpret experimental data;
  • write scientific reports; and
  • present their research to an academic audience.

Entry requirements

Students should have experience in chemistry, or a chemistry-related subject, from their undergraduate degree.

Chemistry 03 
Molecular probes to image inflammation 

Supervisor

Professor Stephen Faulkner

Description

This project will involve making stable metal complexes that can be used to image inflammation. You'll makes some simple peptides and attach them to a metal complex that can be used to observe where the complex is localise: the peptides have been chosen to target inflammation in tissue (part of the natural response to injury and disease), and act to direct the whole assembly so that the metal can then be used to explore areas of inflammation. The purpose of the metal ion is to signal the location of the complex by acting as a beacon through luminescence or magnetic resonance imaging. We will focus on the use of lanthanide ions in this role, using gadolinium complexes as classical MRI contrast agents (which work by changing the relaxation times of bulk water), and using other lanthanides such as terbium and dysprosium that can be addressed by luminescence and by exploiting the large their NMR (and the large chemical shifts induced by their paramagnetism).

Project outcomes

You'll learn a range of synthetic chemistry techniques, from peptide synthesis through to coordination chemistry. In the process, you'll also develop skills in spectroscopy, particularly luminescence and NMR methods. While this project is focused on chemical synthesis and characterisation, you'll also gain experience of collaborating with biomedical scientists.

Entry requirements

You should have experience in Chemistry, or a closely related subject from your undergraduate studies.

Chemistry 04 
Iridium catalysed reductive functionalisation of trifluoroacetamides for the broad scope synthesis of alpha trifluoromethylated amines 

Supervisor

Professor Darren Dixon

Description

Amines and amine derivatives are ubiquitous across the pharmaceutical and agrochemical sectors and therefore new broad scope methods of synthesising desirable but hard-to-access amine classes would have significant impact across the biomedical sciences. Amines possessing alpha trifluormethyl groups are one such group of amines that are notoriously difficult to make by any existing method. In 2015, our group pioneered the reductive functionalisation of amide and lactam functionality as a new strategic way of accessing alpha branched amines. Very recently we invented a new class of reduction catalyst that allows for the first time the reductive activation of trifluoracetamides, thus providing new access to alpha trifluoromethylated amines, after down-stream reaction with carbon centered nucleophiles such as Grignard reagents. This 6-7 week project will build on these exciting proof of concept studies to explore the range of alpha trifluoromethylated amines that can be accessed.

Project outcomes

Objectives: With close supervision and guidance from the PhD student aligned to this project and myself, the objectives of this 10 week project are: To synthesise a small library of trifluoroacetamides from acyclic and cyclic amine substrates. To investigate and find the optimal conditions for the efficient reductive activation of those substrates. To explore the range of Grignard reagents that will efficiently couple with the reductively activated intermediates. To contribute to the scope of this new reaction and characterise the new made reaction products. Thus, this 6-7 week project will provide a very well defined training and research package and the arising results will very likely contribute to a high quality and impactful paper.

Entry requirements

Organic chemistry knowledge (essential), Organic Synthesis knowledge (essential), basic laboratory skills (desirable but not essential).

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Computer Science

Computer Science 01 
Using deep generative modelling for Bayesian Optimization 

Supervisor

Dr Seth Flaxman

Description

Bayesian optimization is a global optimization tool which is able to work with black-box functions without explicitly assuming any functional form. Instead, it uses surrogates of such a function, commonly expressed in the form of a Gaussian processes (GPs). GPs, however, pose computational challenges which might become prohibitive in real-life applications. Recent publications have proposed methods using deep generative modelling, and in particular, the variational auto-encoder technique, to approximate GPs in a highly efficient manner. This project will use pi- and PriorVAE models as surrogates to perform Bayesian optimization allowing for more rapid and efficient computation.

Project outcomes

Participant will work with variational auto-encoders, learn about Bayesian optimisation, and propose a new, fast and efficient computational routine for BO. Participant will then produce a technical report based on the findings which would be posted on arXiv. The report will form the basis of a computer science conference submission.

Entry requirements

Good understanding of statistics and fluency in Python would be beneficial. However, completing the project in R would also be a possibility.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Computer Science 02 
Assessing deep generative models of spatial data using Simulated-Based Calibration 

Supervisor

Dr Seth Flaxman

Description

Evaluation of approximate methods, especially when it concerns spatial data, is a hard task. Spatial data is described by capturing correlations between locations and, henceforth, relies on the notion of Gaussian processes (GPs). GPs, while being very flexible, provide a computational challenge. A series of novel methods has been recently proposed, allowing to approximate GPs in the Bayesian inference setting by highly efficient models, expressed via variational autoencoders (VAEs). Simulation-based calibration (SBC) is a practical method allowing to validate computationally derived posterior distributions or their approximations. The goal of this project is to use SBC for assessing methods, as well as to find the optimal set of hyperparameters to train the VAEs.

Project outcomes

Participant will work with variational auto-encoders, learn about Bayesian inference of spatial data, and construct a computational procedure for assessing VAE-based models to approximate Gaussian Processes. Participant will then produce a technical report based on the findings which would be posted on arXiv. The report will form the basis of a computer science conference submission.

Entry requirements

Good understanding of statistics would be beneficial. The project could be completed both in Python or R. The route using R would rely on an existing R-based package to perform the SBC routine; the variational autoencoder will need to get implemented in R (eg using R interface to Torch). Python implementation can rely on the existing VAE code, while the SBC routine will need to get created.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Computer Science 03 
Conformal predictions for experimental design  

Supervisor

Dr Seth Flaxman

Description

The adaptive experimental design methods balance exploitation and exploration, and have been applied on various sequential decision-making applications. Adaptively predicting uncertainties is an important and challenging step. Recently, conformal predictions provide a distribution-free uncertainty quantification technique through statistically rigorous uncertainty sets/intervals. This project will study how to incorporate conformal predictions techniques with experimental design in theory and practice.

Project outcomes

Participant will learn about experimental design, Bayesian inference, conformal prediction and estimation of uncertainty. Participant will then produce a technical report based on your findings which would be posted on arXiv. The report will form the basis of a computer science conference submission.

Entry requirements

Good understanding of statistics is beneficial.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Computer Science 04 
Data recorders for social robots 

Supervisor

Professor Marina Jirotka

Description

To improve robot safety and trust, robots should be equipped with a standard device which continuously records a time stamped log of the internal state of the system, key decisions, and sampled input or sensor data. In effect this is the robot equivalent of an aircraft flight data recorder (aka black box). Without such a device, finding out what the robot was doing and why in the moments leading up to an accident, is more or less impossible. However, it is not the black box on its own, that forms the safety and trust mechanism; it is its inclusion within a social process of accident/incident investigation. An investigation will draw on data recorder information and information from human witnesses and experts to determine the reason for an accident - and lessons to be learnt from it. This project aims to develop and demonstrate both technologies and processes (and ultimately policy recommendations) for robot accident investigation.

Project outcomes

Participants will be involved in interdisciplinary activities concerning the design, development and enactment of a robot related accident scenario and investigation, which will be conducted in the framework of the EPSRC funded project RoboTIPS. They will participate in the design of the scenario, conducting online research and stakeholders’ interviews to define a meaningful robot accident scenario; they will participate in the technical implementation of the EBB on the robot selected for the accident, by collaborating with the RoboTIPS project engineers; they will participate in the enactment of the scenario, by playing roles during the mock accident investigation process; and finally they will collaborate with researchers on the data collection and analysis, by conducting qualitative and quantitative analysis.

Entry requirements

None. Preferable: Knowledge/ prior education in computer science, robotics, law, social sciences, possibly performing arts.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Computer Sciences 05 
Application of Machine Learning Methods to research problems in computational biology 

Supervisor

Professor David Gavaghan

Description

Time series models are used to approximate the dynamic behaviour of dynamical systems and are ubiquitous across mathematical and computational biology. Embedded within the models are parameter values which govern model outputs and must be inferred from experimental data. Research in our group focuses on the development of machine learning algorithms for parameter inference in applications ranging from the modelling of pandemics, through the safety of new drugs, to the potential use of enzymes in developing biofuels. Underpinning all of our research is our PINTS (Probabilistic Inference on Noisy Time Series) open-source software, which provides to the user an easy-to-use interface to the machine learning and optimisation algorithms that we have implemented within PINTS. Depending on the interests of the interns, summer internship project might involve either further development of one of more algorithms within PINTS, of the application of machine learning techniques to one of the scientific problems that are of interest to our group.

Project outcomes

An understanding of modern machine learning methods, improved Python programming skills, possible scientific results depending on choice of project.

Entry requirements

Some experience of computer programming with a preference for Python. An interest in software engineering and mathematical modelling in biology.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

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Earth Sciences

Earth Sciences 01 
River health check - Tracking nutrient and metal transport in the upper River Thames, Oxford 

Supervisor

Professor Robert Hilton

Description

Rivers collect and transport elements from across landscapes, and carry them dissolved in their flowing water. These dissolved components of river water can come from natural reactions happening in soils, and can help tell us about how water moves through the environment. However, in the UK and other countries, human activities play a large role in setting the river chemistry. Farming, industry and urban locations can add dissolved nutrients (eg nitrate, phosphate) and other elements and metals which can impact the water chemistry and life in the river. These element inputs are not constant in time, nor space. This project aims to better understand these natural and human-driven inputs to the dissolved loads of the upper Thames and Cherwell rivers in Oxford. The student will use filtration methods to collect water samples, and use analytical equipment (eg Ion Chromatography) to measure the nutrient and metal loads over a snap-shot during summer flow.

Project outcomes

The outcome will be a new dataset of nutrient and dissolved ion content (eg nitrate, phosphate, calcium, iron, copper etc) for the Thames and Cherwell Rivers. The student will produce a quality-controlled dataset. They will also undertake statistical analysis on the co-evolution of dissolved chemistry in the rivers. The student will also have received training on sampling methods, filtration, and the operation of analytical equipment in the laboratories at the Department of Earth Sciences.

Entry requirements

Relevant degree subjects include Earth Sciences or related theme; interest in field and laboratory work.

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Engineering

Engineering 01 
Mechanical metamaterials with novel elastic behaviour 

Supervisor

Dr Reece Oosterbeek

Description

Additive manufacturing allows engineers to design and make porous lattice structures with highly tuneable mechanical properties. Still, due to the coupling between lattice material properties (e.g. strength and stiffness), achieving suitable combinations of mechanical properties for applications such as orthopaedic implants remains a challenge. Mechanical metamaterials are designed materials with properties not found in nature, which arise from their structure rather than composition. Recent work in this area has developed mechanical metamaterial designs based on additively manufactured titanium that combine high compliance with high strength, enabling material design with unprecedented levels of control. This project will use mechanical testing and digital image correlation to study the tensile and compressive mechanical properties of these novel metamaterials. We aim to demonstrate control of mechanical properties and understand the underlying deformation behaviour of the metamaterial structure influencing these properties.

Project outcomes

You will receive training in mechanical testing and digital image correlation, collect data, and perform analysis at a level suitable for publication.

Entry requirements

Experience in materials science or engineering at undergraduate level, in particular mechanical testing, would be useful.

Engineering 02 
Bubble Mediated Green chemistry 

Supervisor

Professor James Kwan

Description

Ultrasound activated bubbles (i.e., cavitation) enables unique chemistries at ambient conditions. It has been proposed as an avenue for greening chemistry by reducing energy requirements to produce high-value products. This project aims to use ultrasound and bubbles to enable selective oxidation of an alcohol to an aldehyde and compare it to other methods.

Project outcomes

Demonstration of selective oxidation of benzyl alcohol to benzaldehyde as measured by high pressure liquid chromatography using different methods (thermal- and sonochemical). Compare the energy requirements to achieve a fixed conversion.

Entry requirements

Ideally an engineering or chemistry background.

Engineering 03 
Adversarial models of data structures in network switches  

Supervisor

Professor Noa Zilberman

Description

Internet traffic volume is ever increasing, with network switches processing today up to 50 Terabits/second and more than 10 billion packets/second. However, network switches are extremely resource constrained, with less than 1% of the amount of memory available to a typical CPU. To overcome this challenge, monitoring tasks in programmable switches use compact data structures (e.g., sketches) to filter and summarize flow and packet statistics. However, attackers can attempt to modify the values in the data structures, allowing DDoS attacks avoid detection. The goal of this project is to study compact data structures implementable in programmable switches and develop adversarial models that cause the data structures to perform incorrectly. This project covers topics in computer networks, programmable hardware devices and cyber-security.

Project outcomes

You will gain skills in advanced computer networks, performance benchmarking, and programming for resource- constrained devices. You will experience working with cutting-edge platforms such as terabit-switches and smartNICs.

Entry requirements

Programming skills, preferably Python are required. Basic knowledge in computer networks is required. A relevant degree in engineering, computer science or related subjects is desirable but not essential.

Engineering 04 
Sustainable Cloud Computing 

Supervisor

Professor Noa Zilberman

Description

Cloud computing is a critical part of our modern way of life. However, cloud computing comes at a high cost to the environment, with companies such as Google and Amazon consuming tens of terawatt-hour every year. There are many ways to improve the sustainability of cloud computing: from carbon-intelligent assignment of computing tasks, to optimization of task scheduling and execution. This measurement-based project will explore trade-offs in power efficiency of different computing tasks, experimenting with a range of applications, resource sharing methodologies and types of equipment. The results will be tied to a carbon emissions model. By the end of the project, we aim to publish the measurements and their analysis as a public repository, with a potential for a conference paper publication.

Project outcomes

As part of this project you will learn about computing infrastructure, cloud computing applications, measurement methodologies, and performance acceleration. You will gain experience in scripting, performance-aware programming, publishing code and artefacts, and - importantly - you will help reduce the carbon footprint of computing.

Entry requirements

Programming skills are required. Preference for previous experience working on Linux. A relevant degree in engineering, computer science or related subjects is desirable but not essential.

Engineering 05 
Personalised Modelling of 3D Printable Hearts 

Supervisor

Dr Abhirup Banerjee

Description

The human heart exhibits considerable inter-person variability in terms of its shape and function, which significantly impacts the effectiveness of cardiac disease prevention, diagnosis, and treatment. Over the past few years, our team has worked on developing novel AI and geometric deep learning-based methodologies to understand and capture this variability in 3D (and 3D+time). In this project, we will employ these technologies and further investigate this 3D shape variability over different subpopulations in terms of sex, age, etc., different diseases such as cardiac stroke, diabetes, etc., and the cardiac cycle (i.e. heart beating). We will also develop a pipeline involving automated postprocessing steps in order to transform the 3D geometric models into personalised 3D printable hearts for better understanding of the cardiac mechanisms over varying groups..

Project outcomes

You will have understanding of the 3D geometry of individual human heart and how it varies over different subpopulations (e.g. sex, age), different diseases (e.g. stroke), as well as during heart beating. The final technical report should be disseminated as a conference or workshop article.

Entry requirements

Some experience in coding, especially Python, is desirable but not essential. Initial training will be provided during the start.

Engineering 06 
Experimental Hypersonic Aerodynamics 

Supervisor

Dr Luke Doherty

Description

This project will be undertaken in the Oxford High Speed Wind Tunnels located at the Oxford Thermofluids Institute. These facilities are used to study fluid flows at speeds in excess of 1 km/s which are experienced by reentry capsules, meteorites and demising satellites. The project will contribute to ongoing experimental programs through analysis of data, design of experimental wind tunnel models and the continued development and use of experimental techniques such as pressure sensitive paint, schlieren, infrared thermography and spectroscopy.

Project outcomes

In addition to supporting the completion of experiments, it is expected that during the summer placement the intern will learn the fundamentals of hypersonic flows, the working principles of the wind tunnels and the difficulties associated with ground testing at velocities in excess of 1 km/s. They will learn about the experimental techniques and advanced instrumentation that are used to investigate the flows, including the use of cameras capable of recording up to 5 million frames/second.

Entry requirements

No specific entry requirements; necessary skill training will be provided. Aptitude is more important. Relevant degrees include Engineering, Physics or Maths degrees or equivalent training that includes data analysis using Matlab/Python etc.

Engineering 07 
Learning Reward Attribution for General-Sum Games 

Supervisor

Professor Jakob Foerster

Description

Learning in general-sum settings (i.e. the iterated prisoner’s dilemma) is an open frontier for the AI community. Usually, in this setting we assume that the reward functions of individual agents are fixed and that as AI scientists we need to find methods that allow agents to perform well given these individual rewards. This has led to methods like LOLA etc. However, even if individual reward functions can be changed by the designer, this is not necessarily a trivial task: While summing up all of the rewards completely gets rid of the social-dilemma aspect and makes the problem fully cooperative, it does so at the cost of creating a very challenging credit assignment problem. This project will investigate reward attribution and shaping methods for large-scale general-sum settings.

Project outcomes

If successful, this project will result in new methods and publications.

Entry requirements

Python Programming.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Engineering 08 
Robust Continual Learning: Effective Learning from Adversarial Streams 

Supervisor

Dr Adel Bibi

Description

Continual learning is an open problem with various real-world applications. While there is a large body of work on learning from large corpus of static well-curated data, learning from streams where the distribution of the data varies per time step is a several folds more challenging problem. We seek to formulate settings where there is an adversary in the loop that can potentially alter or perturb the samples presented by the stream. The adversaries goal is to break continually learning algorithms without being detected. On the other hand, efficient robust continual learners can preserve a high accuracy even under the worst case scenario where every sample presented by the stream has been adversarially manipulated. We want to formulate this new exciting problem and propose effective robust continual learning algorithms under such challenging settings.

Project outcomes

'The target for this proposal is to work on the problem formulation, implement and benchmark several existing algorithms, and propose a new robust continual learner that works in this stream. Ideally, we should aim to have a good initial draft for a tier-one conference future submission.

Entry requirements

Basic understanding of machine learning, linear algebra, and probability.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Engineering 09 
Multi-Modal Partially Labelled Stream 

Supervisor

Dr Adel Bibi

Description

Data on large systems is often stream lined and multi modal, e.g., textual, images, videos, and or sound. All this data is being accumulated while jointly changing in distribution. Moreover, much of this data presented from the stream is only partially labelled. We seek to study the problem of training models on a partially labelled streams in multi-modal setting. In particular, we seek to find new effective algorithms to performing joint self-supervised continual learning on the unlabelled data while learning in supervised fashion the labelled portion of the stream.

Project outcomes

'The target for this project is to study existing works on multi modal learning from stream, benchmark existing algorithms, and implement and effective methods using a single architecture following the recent advances in oneTransformer network, i.e., one network solving multiple multi-modal tasks.

Entry requirements

Basic understanding of machine learning, and good working knowledge in linear algebra and probability.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Engineering 10 
Modelling Delayed Labels in Online Continual Learning 

Supervisor

Dr Adel Bibi

Description

Online continual learning is the problem of predicting every sample in the stream while simultaneously learning from it. That is to say, the stream first presents data to be predicted and then the stream reveals the labels for the model to train on. However, in most real-case scenarios, labelling is an expensive laborious and time-consuming procedure. Thereof, we seek to study the sensitivity of existing continual learning algorithms when labels of images at step t are only provided at step t + k. This setting poses two challenges:

  1. Learning from unlabelled data; and
  2. Modelling the delayed labels.

To that end, we are interested in proposing new algorithms that per time step t can perform self-supervision continually while jointly training on the labelled data revealed from step t-k.

Project outcomes

Working draft for a tier-one conference submission showing how existing methods perform under delayed evaluation.

Entry requirements

Basic machine learning, probability, and linear algebra knowledge.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Engineering 11 
Making machines understand and generate humour 

Supervisor

Dr Adel Bibi

Description

Humour as a fundamental human expression can be triggered by different kinds of stimuli eg facial expressions, poses, languages, and speeches. Analysing how those modalities quantitatively attributed to the sense of humour is an important topic, and a good first step to allowing machines to understand humans. Existing methods have collected datasets with multiple modalities and trained models with multi-modality interaction to localize the punchline in a video and estimate how funny it is. By visualizing the attention maps among different modalities, these methods manage to somewhat understand how much each modality contributes to the punchline. However, these methods still need to collect the training data carefully and manually annotate the location of the punchline, which makes it hard to scale up. Also, the current approaches only treat humour detection as a binary classification problem which hardly follows the intuition that humour has different levels.

We will have a variety of projects related to understanding and generating humour. Instead of understanding the composition of humour through supervised learning, we propose to understand it through the lens of either foundation models or through recent large generative models trained on vision and language. This could be feasible by exploiting the recent CLIP model for image-text interaction and a Language Model (LM) like GPT-3 on top of it to generate the funny caption we want. Since both CLIP and LM can be trained with either unsupervised approaches or unpolished text-image pairs crawled from the Internet, the limitation on the data scale can be then resolved. Meanwhile, as both LM and CLIP are implemented with Transformer-based architectures, it is possible to quantitatively and qualitatively analyse the contribution of each composition. The funny text generation itself conditioned on image inputs can be also an interesting application in the field of visual-language interaction. This project can also shed some light on future works. For example, we can exploit the correlation between different modalities as prior knowledge to generate funny images or video clips with the integration of generative models like diffusion models and GAN. Besides, the analysis result could provide insights into cognitive science to understand how humour correlates with physical stimuli.

Project outcomes

The target of this project is to first survey existing works in this area and understand what is the most recent research in the field and reproduce results on existing papers. A new clear problem definition formulation is needed to incorporate multi-modality  and implement and benchmark existing methods. Towards the end, and after we have a full grasp on what works under this challenging domain, we shall consider both generative and supervised approaches towards multi-modal humour generation for various supervised tasks.

Entry requirements

Python and particularly strong familiarity with packages like pytorch. Good understanding of linear algebra, probability, and machine learning.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

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Materials

Materials 01 
Alloy Nanoparticles for Electrochemical Hydrogen Production 

Supervisor

Professor Robert Weatherup

Description

The production of hydrogen by electrochemical splitting of water offers a zero-carbon method for converting renewable energy to a fuel which can be stored for when it is needed and used to replace fossil fuels in many industrial processes. To produce hydrogen efficiently, electrocatalysts are needed that reduce the overpotentials for the hydrogen and oxygen evolution reactions (HER/OER) and thus avoid large amounts of energy being wasted. They must also remain stable over extended periods under the aggressive electrochemical conditions they typically operate under. The best performing electrocatalysts are based on Platinum and Iridium, whose scarcity and cost are limiting. There is thus a pressing need for the development of low-cost electrocatalysts based on earth-abundant elements. This project will focus on depositing alloy nanoparticles using a state-of the-art Nikalyte NL-UHV instrument, which allows their composition and size to be precisely tuned. Following sputtering, the nanoparticles will be characterised using scanning electron microscopy (SEM) to determine their size distribution, and their electrochemical performance will also be tested.

Project outcomes

The project will develop processes for producing elemental and alloyed nanoparticles, with the candidate gaining experience in physical vapour deposition and high vacuum techniques. The nanoparticles deposited will be characterised electrochemically with a three-electrode cell. Further to this scanning electron microscopy (SEM) will be used to characterize the size of the nanoparticles. The candidate will also gain experience working in a multidisciplinary group as well as coordinating with other PhD students and postdocs within the group working on similar research.

Entry requirements

Undergraduate degree-level understanding of characterisation techniques for studying nanomaterials (prior practical experience not necessary).

Materials 02 
Synthesis of High Entropy Transition Metal Dichalcogenides 

Supervisor

Professor Robert Weatherup

Description

Transition metal dichalcogenides (TMDCs) are a class of materials that are of great interest for catalytic and energy storage materials. TMDCs consist of a metal and one of sulphur, selenium or tellurium, in a ratio of 1:2 metal:dichalcogenide. Recently, high entropy transition metal dichalcogenides, consisting of TMDCs composed of multiple metals dispersed in a single material have attracted a great deal of interest. This project seeks to synthesise a variety of high entropy TMDCs, and use x-ray diffraction, Raman spectroscopy and electrochemical analysis to reveal trends in the performance of these materials as electrocatalysts, and how this varies with composition.

Project outcomes

Students will synthesise a range of high entropy TMDCs, gaining experience in high-temperature synthesis and working with high vacuum equipment. Students will also gain experience in lab-based glass ampoule manufacture and widely-applicable material characterisation methods.

Entry requirements

Undergraduate level understanding of materials characterisation techniques.

Materials 03 
Linear Friction Welds in Titanium Alloys for Aero-engine applications 

Supervisor

Professor Angus Wilkinson

Description

Linear friction welding (LFW) of titanium alloys give the potential for significant weight saving in construction of fans in the entry section of aero-engines. The weight saving drives a reduction in CO2 emissions. Demonstrating acceptable mechanical response of such LFW is a vital part of the safety case. This project will look at the local changes in mechanical properties close to a linear friction weld (LFW) joining two blocks of Ti-6Al-4V in different microstructural states. We will use digital image correlation to measure the spatially varying time dependent plastic strain response across the LFW when placed under different mechanical stresses. We will also use some optical and electron microscopy analysis to examine the microstructure and crystallographic texture changes across the LFW.

Project outcomes

You will receive training in mechanical testing, digital image correlation, optical microscopy and image analysis and the use of Matlab, and then work independently with these methods. With help from group members you may also use SEM, EBSD and nanoindentation analysis to study your samples. Data collection and will be at a level suitable for a journal publication. You will present results to the Oxford Micromechanics Group.

Entry requirements

Experience in materials science, mechanical or aerospace engineering or physics and basic knowledge of deformation and microstructure from an undergraduate degree are desirable but not essential.

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Mathematics

Maths 01
Polynomial Conservation Laws of Biochemical Reactions

Supervisor

Dr Hamid Rahkooy

Description

Biochemical reaction networks with mass-action kinetics can be models by ordinary differential equations (ODEs) that measure the change of concentrations of species with respect to time. Conservation law of such networks are defined to be the first integrals of the associated ODEs. Linear conservation laws are quite well-known. However, polynomial conservation laws are not well-studied yet. In this project, we are interested in finding polynomial conservation laws, using techniques from computational algebraic geometry, such as Groebner bases. We aim at finding efficient algorithms for computing them. As the ODEs describing biochemical reactions depend on some parameters, we are also interested in identifying the parameter region that correspond to different conservation laws.

Project Outcomes

The candidate will learn about computational algebraic geometry techniques, apply them for finding conservation laws of biochemical reactions. The existing results and algorithms will be improved and a package will be written, implementing the results, in a computer algebra system, eg, Macaulay 2, Singular, SymPy.

Entry requirements

None.

Maths 02
Numerical black hole metrics using machine learning

Supervisor

Dr Christopher Couzens

Description

A metric is the fundamental object to consider when studying spacetime. It has many uses and is used to define time, distances and volumes in the spacetime. The components of the metric satisfy a set of coupled partial differential equations (PDEs) known as Einstein's equations (or further generalisations). In general they are very non-trivial to solve with only a few closed form solutions known. In this project we will study a class of metrics which describe rotating black holes in five-dimensions and attempt to use machine learning to numerically solve the difficult PDEs for the metric.

Project Outcomes

As a preliminary introduction to the topic the participant will learn about five-dimensional gauged supergravity and the black hole solutions that can be found within the theory. (Gauged supergravity = Einstein's general relativity + coupling to electromagnetism + imposing an additional symmetry called supersymmetry.) They will then learn some basic machine learning and how to apply it to numerically solve PDEs. They will apply these techniques to first recover the metrics of known black hole solutions before using these techniques to look for new solutions. At the end of the project the participant should have been exposed to new research in the fields of theoretical physics and machine learning.

Entry requirements

You should have some experience in mathematics and physics. Basic programming skills would be useful however this is not essential.

Maths 03
De Giorgi onjecture and Nonlinear Liouville Type Theorems

Supervisor

Dr Immanuel Ben Porat

Description

A long standing conjecture due to De Giorgi states that any bounded, smooth and increasing (with respect to some variable) solution to the Allen-Cahn equation must be 1-dimensional, at least in dimension less then or equal to 8. This conjecture has been a source of various exciting developments in the broad area of non linear Liouville type theorems. The aim of this project is to gain a better understanding of the existing results for dimensions at most 4, as well as a better understanding of the difficulties which arise in tackling the conjecture in higher dimensions.

Project Outcomes

  1. To gain a solid understanding of classical and modern results related to non linear Liouville theorems; and
  2. To gain knowledge that will set the grounds for doing work at a research level.

Entry requirements

Undergraduate degree in pure or applied mathematics.

Maths 04
Teaching Lie algebras with homotopy operations to a computer

Supervisor

Dr Lukas Brantner

Description

Given a manifold M and a positive integer k, let B(M;k) be the configuration space of k (unordered) non-colliding particles in M. These spaces are of interest in mathematics and theoretical physics, and it is a classical challenge to compute their topological invariants.Much progress has been made for rational cohomology, a topological invariant which is based on the rational numbers. Less is known for other topological invariants, in particular cohomology based on the set of numbers {1,…,p} with modular "clock arithmetic”. Recently, certain new algebraic structures, namely Lie algebras with homotopy operations, facilitated several advances on this topic. The aim of this project is to write a computer program which can handle Lie algebras with homotopy operations (and ideally also compute their cohomology). This would allow us to quickly describe the E2-page of a spectral sequence computing new invariants of configuration spaces.

Project Outcomes

A program for Lie algebras with homology operations. Maybe a research paper.

Entry requirements

Relevant degree subject: Mathematics. Required: Knowledge of basic algebraic topology (including the definition of homology groups) and interest (or even basic experience) in coding.

Maths 05
Scaling Graph Neural Networks

Supervisor

Professor Harald Oberhauser

Description

Graph Neural Networks (GNNs) can process structured data that appears in the form of graphs. GNNs have found many applications in recent years, ranging from drug discovery to the study of social networks. Despite these often spectacular successes, the training of GNNs is costly and typically requires many GPU hours, making scaling GNNs to capture bigger datasets an ongoing challenge. The focus of this project is on developing techniques to make these types of networks more efficient and effective at processing larger and more complex datasets. This might involve exploring a combination of existing methods or even developing new approaches to reduce the number of calculations required by the network without sacrificing accuracy.

Project Outcomes

The project outcome is an overview and comparison of the existing approaches to scaling GNNs. This should include a theoretical study and literature review but also a software package that evaluates the performance of the scaled graph neural networks on a variety of tasks, including node classification, link prediction, and graph classification, to demonstrate their improved ability to handle larger, more complex graphs.

Entry requirements

Undergraduate courses in probability theory. Some programming experience.

Maths 06
Modelling and simulation of crowds

Supervisor

Dr Rafael Bailo

Description

This project will explore pedestrian dynamics (the study of people moving in crowds) numerically. We will implement one or several existing agent models for crowds (differential-equation-based models), either in Python or in Julia. With this implementation, we will reproduce known behaviours of pedestrians, such as lane formation in crowded corridors. Time permitting, we will explore either new models or a systematic calibration of the existing models based on real world data.

Project Outcomes

You will implement at least one pedestrian model during the project. Further to this, and depending on your interests, you will concentrate your work on developing a new model, performing a calibration of an existing model, or constructing advanced visualisations for your implementation, such as the Social Force Model. In each case, you will have developed code, performed some analysis, and produced visualisations, all of which will be tangible evidence of your efforts.

Entry requirements

Experience and knowledge in programming would be useful.

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Physics

Physics 01
Computational analysis of the RNA sequence-structure map

Supervisor

Dr Nora Martin

Description

Molecular sequence-structure maps are important for evolutionary processes since they determine how mutations, which change a molecular sequence, influence the molecular structure, which is relevant for molecular function. In this project, you will work with an established computational model, which describes the relationship between RNA sequences and their folded secondary structures. There are several directions this project could take, depending on the student’s interests, but broadly the aim will be to pick one biologically relevant property of this sequence-structure map and quantify this property for a large sample of sequences.

Project Outcomes

You will learn and apply quantitative and computational research methods. Scientifically, the project will contribute to our understanding of the sequence-structure map of RNA secondary structure.

Entry requirements

You should be studying/have studied either a quantitative discipline (physics/maths/computer science or similar) and have an interest in biological questions or a background in biological sciences and strong quantitative skills. Experience in programming, especially in Python, is useful but not essential.

Physics 02
Satellite Measurements of Atmospheric Composition

Supervisor

Dr Anu Dudhia

Description

The IASI instruments on the MetOp satellites routinely measure the infrared spectrum emitted upwards from the Earth. From these spectra we can occasionally identify anomalous signatures associated with large concentrations of various gases emitted in pollution events. The project is to study these in more detail.

Project Outcomes

A brief report summarising the occurrences of large concentrations of a chosen molecule, with some background on the processes that might explain these events.

Entry requirements

Reasonable computing skills, python preferred.

Physics 03
Counting planets over cosmic time

Supervisor

Professor Chris Lintott

Description

In the last few decades, astronomers have used a variety of techniques to discover thousands of exoplanets in stars around the Sun, proving that such worlds are an extremely common feature of our cosmic environment. It is still an open question, though, whether the Solar System is unusual, and whether the Earth has rare features which make it a suitable home for life. The aim of this project is to contribute to this work by calculating the number of different types of planets in the Milky Way as a function of time. It will use cosmological simulations of Milky Way like galaxies, and emerging models of the relationship between star and planet formation. It would suit a student with a broad range of interests in astronomy, and requires moderate Python programming skills as well as the ability to explore the astronomical literature.

Project Outcomes

The aim of the project will be to produce a short (three page) submission to the Research Notes of the American Astronomical Society, and to give a presentation to astronomers in Oxford about the outcomes of the research.

Entry requirements

Experience with, or a desire to learn, Python will be needed.

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Statistics

Statistics 01
Mutation discovery with machine learning

Supervisor

Professor Charlotte Deane

Description

The main goal of the project will be to integrate methods for identifying negative mutations in genome databases into an open source pipeline. As a result, it will be possible to run advanced machine learning predictors on novel genome sequencing samples with no prior knowledge required.

Project Outcomes

You will contribute to an open source software package which will be made widely available, and write the relevant documentation.

Entry requirements

Programming skills, preferably Python are required. A relevant degree in mathematics, computer science, software engineering, or bioinformatics-related subjects is desirable but not essential.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Statistics 02
Machine Learning and AI for SARS-CoV-2 Main Protease Inhibitor Discovery

Supervisor

Professor Garrett Morris

Description

You will learn about how to apply the latest machine learning and AI technologies to help discover new inhibitors of a key drug target in SARS-CoV-2, the virus that causes COVID-19. By training models on binding data and 3D atomic structures of inhibitors of SARS-CoV-2 main protease, you will advance our understanding of how to block viral maturation and how to develop new drugs to treat COVID-19.

Project Outcomes

You will explore data from the COVID Moonshot project, as well as explore a variety of classical ML models and more advanced methods such as Graph Neural Networks, Atomic Environment Vector-based models, and molecular Transformers such as Uni-Mol.

Entry requirements

Python is essential; a background in mathematics, statistics, computer science, biochemistry, chemistry or physics.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Statistics 03
Theory of Gradient Descent for Learning Problems

Supervisor

Professor Patrick Rebeschini

Description

Gradient descent (GD) and its stochastic variant (SGD) are fundamental algorithmic paradigms in machine learning, ubiquitously used to train various types of learning models. Despite their popularity, there are many open questions in learning theory related to their generalization capabilities, for instance, related to the benefits of stochastic noise. Recently, exciting separation results between GD and SGD have been established in the literature. In the general setting of stochastic convex optimization, it has been shown that, in the worst case, GD requires 1/ε^4 iterations to achieve a solution with ε excess risk while SGD always requires at most 1/ε^2 iterations. That is, to output a solution with a statistical accuracy of ε = 0.1, SGD needs at most ∼ 100 iterations while GD might require at least ∼ 10000 iterations. The goal of this project is to investigate novel types of separation results between different methods based on implicit regularization in various settings of interest in modern statistics and machine learning.

Project Outcomes

Literature review and design and analysis of novel algorithmic principles, both from a theoretical and empirical (ie running numerical experiments in Python or Julia) point of view.

Entry requirements

None.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Statistics 04
Automating Reinforcement Learning

Supervisor

Professor Yee Whye Teh

Description

Reinforcement learning (RL) is a powerful paradigm for training AI agents with many successes across a variety of domains and tasks (eg AlphaGo, Starcraft, Atari). However, RL algorithms are notoriously unstable and sensitive to configurations which require extensive tuning. To address this challenge, recent work in automated reinforcement learning (AutoRL) has sought to automate some of these design choices. In some settings, AutoRL algorithms have also produced configurations that perform far better than manual tuning. During the project, you will understand the scope and limitations of current AutoRL approaches, and explore new opportunities for improving the efficiency and performance of RL algorithms.

Project Outcomes

You will learn about automated machine learning and reinforcement learning, and produce a literature review. You will learn Python and a number of machine learning frameworks in order to implement standard AutoRL algorithms, to use computational experimentation to test out ideas and optimise programmes, and to work collaboratively in a group. You will write a final report on your findings. It is hoped that the project will lead to a novel methodology in automated reinforcement learning or a useful publicly available codebase.

Entry requirements

Exposure to machine learning course during undergraduate degree.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

The Department of Statistics also offers several other projects as part of their own Statistics Summer Research Internships programme. This programme is separate to UNIQ+ and has it's own application process that is detailed on the department's website.

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Projects in Life and Medical Sciences are offered by the following departments:

Biochemistry

Biochemistry 01
Fluorescence imaging of DNA loci in cells

Supervisor

Professor Neil Brockdorff

Description

Organisation of DNA along the length of a chromosome includes the formation of A and B type compartments which correspond to large regions that have either high or low gene density respectively. Both types of compartment cluster in 3-dimensional space, driven by dynamic interactions between widely separated sites. The lab are using live-cell fluorescence imaging of selected sites on chromosomes to define compartment dynamics and understand their role in a process termed X chromosome inactivation. The project will involve designing and assembling a DNA construct to introduce a fluorescent tag at a site of interest and some work with established cell lines, collecting and analysing live-cell fluorescence image datasets.

Project Outcomes

You will have the possibility to develop skills and knowledge in molecular biology, mammalian cell tissue culture, live-cell fluorescence imaging, and image analysis. The work will contribute to research into fundamental mechanisms in chromosome biology.

Entry requirements

Applicants should have a good grounding in cell and molecular biology and an interest in chromosomes and/or gene regulation

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Biochemistry 02
Chromosomes in humans

Supervisor

Dr Martin Cohn

Description

Integrity of the genome is critical to both the development and health of humans. Our chromosomes are constantly exposed to various types of DNA damage, which if unrepaired can cause diseases such as cancer. To meet these challenges, several DNA repair pathways have evolved. Our laboratory is focused on understanding how these pathways work in human cells. We have discovered several new proteins playing important roles in these pathways. By applying state-of-the-art techniques in biochemistry, molecular biology and cell biology, combined with world-class mass spectrometry and high-resolution live-cell imaging and structural biology, we are continuously elucidating the role of new components of the DNA repair pathways. Examples of methods used are recombinant protein purification, in vitro assays, CRISPR/Cas9 genome engineering, various cell-based assays including sophisticated live-cell imaging, and cryo-EM.

Project Outcomes

Our students obtain thorough training and accrue important findings relevant for our understanding of genome stability in human cells.

Entry requirements

Applicants should have a good knowledge of cell and molecular biology and an interest in chromosome biology.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

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Biology

Biology 01 
Evolving viruses to target antibiotic resistant bacteria 

Supervisor

Professor Craig MacLean

Description

Antibiotic resistance in pathogenic bacteria has emerged as a fundamental threat to human health. One strategy for combatting antibiotic resistance is to use viruses that infect bacteria (phage) to suppress populations of antibiotic resistant pathogens. In this project you will screen a large a collection of phage to identify phage that can infect antibiotic resistant bacteria, and then evolve these phage in the lab to try and generate improved phage that show enhanced killing of antibiotic resistant bacteria. This project will employ simple tools from microbiology, and it is suitable for anybody with a background in microbiology, evolutionary biology or ecology.

Project outcomes

This project will hopefully generate a series of phage that can be used to effectively kill antibiotic resistant bacteria. The interns will have the chance to present their work as a project report and a seminar, and the data might be published as an publicly available article. 

Entry requirements

There are no specific entry requirements.

Biology 02 
Engineering genetic circuits to drive plant cell responses to environmental stimuli 

Supervisor

Dr Francesco Licausi

Description

Synthetic biology allows us to assemble biological components to perform novel or improved functions in cells. This strategy can be used to engineer plants to be better prepared to cope with environmental challenges, such as those connected with climate change (drought, floods, temperature extremes). In our lab, we use a single cell system (protoplasts) to test the efficacy of novel genetic circuits to elicit responses to specific cues. This project will involve nucleic acid extraction and gene expression quantification, gene cloning and cell transformation.

Project outcomes

This project will allow us to identify biological components of signalling pathways (kinases, transcription factors and promoters) that, rationally integrated, produce an output of the desired magnitude and the timeframe required to drive cell adaptation to desired stimuli (e.g. light, temperature or nutrients) . The student will learn the basic molecular biology techniques for gene cloning, specific transcript analysis through realtime RTqPCR and protoplast transformation. 

Entry requirements

Basic knowledge of biological processes (DNA and protein synthesis).

Biology 03 
How insects respond to temperature change 

Supervisor

Dr Anna Vinton

Description

To conserve species biodiversity alongside expected changes in the climate, it is vital to understand how species respond to changing temperatures in both the short and long term. However, this task is not trivial since temperature alters organisms at multiple scales, from molecules to whole ecosystems. Upscaling the effect of temperature change from molecules to entire ecosystems is not linear, and thus is complicated by dynamics such as changes in individual behaviours, and changes in what species are interacting. Recent scientific progress in genetics, evolutionary biology, ecology, and mathematical modelling make this the optimal time to take a multiscale approach to understanding how organisms respond to temperature increase. In this project we use a fruit fly model system to investigate how increasing temperature alters individuals, within their lifetime as well as their resulting generations. To do this we expose fruit flies to a variety of temperature treatments, and assess their response. Thus the student will learn to collect data about fruit fly fecundity, and lifespan, as well as use a microscope to analyse their morphology.

Project outcomes

You will be an integral part of ongoing work targeted at understanding how species can adapt to climate change. You will also gain basic data analysis skills and laboratory skills including reading scientific papers, caring for the insects, and using a microscope to observe and take pictures. Ultimately your work will be included on a peer reviewed publication resulting from this work. 

Entry requirements

Background in Biological Sciences or a related subject.

Biology 04 
Signatures of selection on cheating 

Supervisor

Dr Laurence Belcher

Description

Many infecting pathogens rely on social traits to survive and thrive, including the production of extracellular molecules that help them scavenge for nutrients within their hosts. This is a form of cooperation – where individuals pay a cost to produce a trait that benefits the whole group. Such cooperation is however vulnerable to cheats, who don’t contribute to a social trait, but still reap the benefits from the cooperation of others. These cheats can outcompete cooperators and spread rapidly within a population. In this project you will learn the bioinformatics tools that allow us to find cheats by analysing bacterial DNA sequence data, and the molecular genetics tools that allow us to discover how cheats are evolving in nature.

Project outcomes

You will contribute to ongoing research projects in the West group. 

Entry requirements

There are no specific entry requirements.

Biology 05 
AI and Machine learning applications in Epidemiology 

Supervisor

Professor Moritz Kraemer

Description

AI and machine learning offer tremendous opportunities accelerating progress in the sciences. Their application in infectious disease epidemiology however remains limited. This project will focus on developing a mapping of applications of AI and Machine learning to answer the most pressing questions in infectious disease epidemiology to prevent future pandemics and epidemics.

Project outcomes

You will analyse real-world data collected by public health agency and read the literature in machine learning and data-driven approaches. You will then implement simple machine learning algorithms to predict infections across different population groups. You will use your findings and reflect which machine learning methods are useful in epidemiology.

Entry requirements

Experience in R and/or Python would be desirable but not essential. We also welcome students with experiences and interest in public health related issues.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Biology 06 
Role of the microbiota in bacterial infection 

Supervisor

Dr Emily Stevens

Description

Host microbiota components play an important role in infectious disease. In many cases, the microbiota protects against infection onset, however, in some cases it can make infection worse. For this project you will study the infection dynamics of the bacterial pathogen Staphylococcus aureus in the presence of different single-species and multi-species microbiota communities. You will use a range of microbiological techniques in laboratory-based experiments, including aseptic (sterile) technique, culturing bacteria and competition assays. You will also conduct infection assays using the nematode worm C. elegans, which we use as a model host to study bacterial infection dynamics.

Project outcomes

You will gain experience conducting laboratory experiments in the field of microbiology. The outcome will be to determine how different combinations of microbiota components contribute to the onset and severity of bacterial infections in a model host system.

Entry requirements

Best suited to students with a background in Biology.

Biology 07 
Introduced birds as a reservoir of disease on remote islands 

Supervisor

Dr Sonya Clegg

Description

Organisms on remote island archipelagos were once protected from disease due to their geographical isolation. Humans have introduced many organisms to these types of environments, bringing diseases with them. Avian malaria has been introduced to remote locations such as French Polynesia via the introduction of exotic birds and mosquitoes. Native species have declined and some face extinction but the drivers are not well understood. Avian malaria may be an important factor, however the extent to which introduced species act as a reservoir of infection is unknown. Blood slides collected from introduced bird species across five islands in French Polynesia would be screened via microscopy for the presence of avian malaria parasites, and potentially other blood borne parasites, to determine prevalence of the disease in different locations and different species. This would provide an assessment of the potential for introduced species to act as a disease reservoir that threatens native species.

Project outcomes

You will have the opportunity to interact with a lab group working on a range of topics on island birds specifically, and bird evolution, conservation and ecology in general. The project involves microscopy to gather the dataset, and you would learn statistical techniques to analyse prevalence data.

Entry requirements

There are no specific entry requirements.

Biology 08 
Evolutionary dynamics of mosquito viruses 

Supervisor

Professor Michael Bonsall

Description

This UNIQ+ project will focus on the replication dynamics of RNA viruses at the within and between-cell level.

Virus cannot immediately be released from infected cells; they must go through several steps before mature virions are produced. The strategy a virus takes through these steps influences the mutation rate, virus yield, the time to infection of new cells and potentially the cell death rate. All these factors influence between-cell transmission dynamics and trade-offs between them may influence virus evolutionary strategies.

We have recently shown, using mathematical modelling, that particular evolutionary dynamics can affect whether viruses evolve to bud from or lyse cells. This UNIQ+ project will build on this work to investigate different drivers of virus transmission and virulence such as trade-offs and within-cell replication events. More broadly, this project will introduce the use of mathematical modelling in approaching research questions in the life sciences.

Project outcomes

The aim will be to develop and analyse a mathematical model of virus replication; this project could run for a single or group of interns.

Entry requirements

Some interest in maths/quantitative skills is required, but specific skills are not expected upfront, other than a willingness to learn.

Biology 09 
Trophy hunting: controversial conservation 

Supervisor

Professor Amy Dickman

Description

Trophy hunting is one of the most controversial issues in conservation, especially with the general public. A fierce debate exists about whether restrictions such as import bans should be imposed, or whether those restrictions would harm conservation and livelihoods. However, there is a surprising lack of empirical data to inform these debates and policies. This project would involve one or more students conducting reviews of national wildlife policies related to trophy hunting, and an examination of aspects such as the number and breakdown of species involved, and evidence for whether trophy hunting helps or harms conservation and livelihoods.

Project outcomes

This project would contribute towards an IUCN situational analysis, which would in turn help inform national and international policy on this topic. The student(s) would be supported in writing accessible articles about their work, as well as having the chance to contribute towards a peer-reviewed paper. This work would have real-world impacts in terms of evidence-based conservation.

Entry requirements

No specific skills necessary

Biology 10 
Impacts of phage therapy on the lung microbiome 

Supervisor

Professor Craig MacLean

Description

The rise of antibiotic resistance in pathogenic bacteria has created the need to develop alternative approaches to treat infections caused by antibiotic resistant bacteria. Phage (viruses that infect bacteria) are the most abundant organisms on earth, and they play a key role in suppressing bacteria in natural environments. Phage therapy is increasingly being advocated as a strategy to combat resistant bacteria, but the impact of phage therapy on commensal bacteria remains poorly understood. The goal of this project will be to assess the ability of phage that target the pathogenic bacterium Pseudomonas aeruginosa to infect members of the respiratory tract microbiome. Pseudomonas aeruginosa is an opportunistic pathogen that can cause serious respiratory tract infection in hospitalized patients and people who suffer from cystic fibrosis. This project will involve testing the ability of Pseudomonas phage to infect a panel of commensal lung microbes. This project will provide training in microbiology, experimental design, and statistics.

Project outcomes

The outcome of the project will be a matrix showing which Pseudomonas phage are able to infect which members of the lung microbiome. If time permits, we will test the ability of phage from commensal microbes to infect Pseudomonas. This project will provide the student with training in microbiology, experimental design and statistics.

Entry requirements

None. Previous experience in microbiology is an asset, but by no means a must.

Biology 11 
Explaining patterns of protective symbiosis 

Supervisor

Dr Alisa McLean

Description

Many insects carry bacterial associates (symbionts) that play a role in immunity against natural enemies such as predators and parasites. Although these symbionts are widespread, they are usually found at intermediate frequency within a species, rather than being universal. We seek to understand the evolutionary and ecological reasons that lead insects to outsource their immunity to a symbiont. This project investigates whether particular protective symbionts are found where risk is particularly high. You will test whether differences in natural enemy preference for particular plants can explain the patterns of symbiont presence (and therefore immunity) seen in nature. To do this, you will carry out behavioural observations and performance assays using insects in the laboratory, and will analyse the data you collect.

Project outcomes

You will conduct a research project that answers a specific scientific question. The project stands alone but is planned as a contribution to a larger peer-reviewed publication (in which it is anticipated you would be involved as an author). You will gain laboratory skills in insect culture, experimental design and behavioural observation, with the opportunity for basic molecular laboratory work (DNA extraction, PCR).

Entry requirements

There are no specific entry requirements. You must be comfortable working with live insects.

Biology 12 
Thermoregulation and the gut microbiome 

Supervisor

Dr Sara Knowles

Description

The mammalian gut contains an extremely dense and diverse community of microbes (the gut microbiome). While an individual's gut microbiome is thought to influence their ability to cope with changing environments, most knowledge about the microbiome's physiological effects comes from studies using laboratory animals, that have limited microbiome variation and do not face environmental challenges. In this project, you will help answer the question - is the ability to thermoregulate in wild animals predicted by their gut microbiome? To do this, they will analyse thermal camera videos from wild mice undergoing a brief cold challenge assay, to characterise how they vary in their response to cold. You will learn techniques in gut microbiome characterisation (e.g. DNA extraction from faecal samples, analysis of 16S microbiome data) and if time permits, conduct statistical analyses linking individuals' gut microbiome profiles with their response to cold to answer the core question at hand.

Project outcomes

The outcome will be an analysis of individual and seasonal variation in the response of wild mice to a cold challenge. You will learn skills in microbiome characterisation, and if time allows can perform statistical analyses to test whether thermal responses to cold are predicted by variation in the gut microbiome. You will produce a short report on the project results and present what you find at a lab meeting.

Entry requirements

There are no specific entry requirements or skills required, but an interest in gut microbiome and/or thermal physiology is desirable.

Biology 13 
Seeing and avoiding wind turbines through a bird's eyes 

Supervisor

Professor Graham Taylor

Description

Wind power is playing a key and growing role in the transition to renewable energy, but wind farms cause significant collision mortality in birds. Given their amazing ability to navigate natural clutter, why are birds susceptible to colliding with wind turbines, and how can we modify wind turbine appearance to address this? The answer to both questions lies in understanding how birds see and respond to their visual environment. Using computer-aided design and a video rendering engine, you will simulate how a wind turbine appears from the perspective of an approaching bird, and explore how simple modifications to its appearance can affect this.

Project outcomes

You will gain a range of transferable research skills, including: (i) training in use of video rendering software to answer scientific questions; (ii) experience of generating and testing novel hypotheses with real-world impact; and (iii) understanding of the interface between bird vision and computer vision. You will prepare a short report or video presentation of your findings, working closely with members of the Oxford Flight Group, and joining the group's weekly meetings to discuss science.

Entry requirements

You will need a background in one of the following subjects: Computer Science, Biology, or Engineering (or a related science subject). Knowledge of Python and experience of using video rendering software or computer aided design software would be helpful to the project, but is not required as training will be provided.

Biology 14 
Machine learning of morphology on microtomographic data 

Supervisor

Professor Graham Taylor

Description

The development of a 3D imaging technique called micro-computed tomography has revolutionised the study of small organisms such as insects, by enabling reconstruction of their internal and external anatomy in exquisite detail. The resulting datasets are too large to be exploited fully by any single researcher, but advances in machine learning have already facilitated the automatic labelling and identification of features in medical tomographic datasets, and are beginning to be used to label features in a wide range of other microscopic 2D and 3D image data. Using open-source image labelling software, you will explore key external morphological features in 3D microtomographic images of insects, classifying these features to enable automated analysis of anatomical data on other samples. The large existing tomographic datasets that you will explore will include a wide range of fresh and preserved specimens, together with amber specimens from the time of the dinosaurs.

Project outcomes

You will gain a range of transferable research skills, including: (i) training in the use of image analysis and labelling software to answer scientific questions; (ii) knowledge of shallow and deep learning approaches; and (iii) understanding of insect morphology. You will prepare a short report or presentation of your findings, working closely with members of the Oxford Flight Group, and joining the group's weekly meetings to talk science.

Entry requirements

You will need a background in one of the following subjects: Biology, Palaeontology, or Computer Science (or a related science subject). Experience of using image analysis software would be helpful to the project, but is not required as training will be provided.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Biology 15 
Data on production practices and agri-food chain sustainability: Cocoa 

Supervisor

Dr Joseph Poore

Description

HESTIA is a project based between the Oxford Martin School and the Department of Biology’s ICCS research group at the University of Oxford. HESTIA provides standardised data and models describing the environmental impacts of agriculture. We enable researchers to contribute and access agri-environmental data, farmers to access our models, and policymakers to access our data. An area of particular focus for us right now is collating more data on the production practices and sustainability of agri-food supply chains. The purpose of this internship would be to upload data to the HESTIA platform, with a specific focus on cocoa production. Cocoa is a useful case study, as its production has been responsible for significant deforestation and greenhouse gas emissions. Improving the sustainability of the cocoa production system could therefore have significant benefits. The sustainable production of cocoa would further have significant socio-economic impacts, thereby addressing the triple bottom line of environment, social and economic improvement. This work would support our recent collaboration with The Waste and Resources Action Plan (WRAP), funded by the Department for Environment, Food & Rural Affairs (DEFRA). You will be helping to systematically generate harmonised and validated data on the average GHG emissions of the cacao tree (Theobroma cacao). Studies for upload will be chosen in advance, to maximise the amount of time for learning and practical upload experience. The day-to-day work would involve reading studies, extracting inventory data and adding these to Excel files in appropriate formats. These data would then be uploaded to the HESTIA platform.

Project outcomes

You will gain knowledge of: cocoa and agricultural production, quantifying agricultural sustainability (in particular using Life Cycle Assessment), and conducting literature reviews. You will gain particular skills in: Excel, Git, and also knowledge of working with basic JSON schemas. You do not need prior skills in these areas and will have opportunities to learn on the job. A basic understanding of these skills would however offer you a faster start. You will also have the opportunity to work with a range of researchers from our team, including environmental scientists, software developers, and behavioural change specialists.

Entry requirements

All aspects can be taught, and my team has a wealth of expertise in training students of diverse backgrounds. However, we are particularly interested in students with interests and enthusiasm for biology, ecology, and environmental sciences, and who are intrigued by quantitative methods, including statistics and ecological modelling.

Biology 16 
Examining the responses of endemic grasslands to climate change 

Supervisor

Dr Rob Salguero-Gomez

Description

Welcome to the Anthropocene, a new era whose hallmark is human impact. Direct and indirect changes we inflict on our natural environment are resulting more extremes. Droughts, fires, flooding are now more frequent than in pre-industrial times. These extremes represent an important challenge to nature, and so to us too, as we depend on nature's ecosystem services. In this project, the student will be involved in an ecology team that is currently evaluating how extremes in droughts, flooding, mechanical, and chemical disturbances shape responses of endemic grassland species in Wytham Woods, Oxford. The student will have a unique opportunity to learn first hand how ecology is done, from theory, to data collection, analyses, and report preparation.

Project outcomes

The student will improve their biological skills, including flora identification, as well as quantitative competence, team work abilities, and scientific writing skills.

Entry requirements

All aspects can be taught, and my team has a wealth of expertise in training students of diverse backgrounds. However, we are particularly interested in students with interests and enthusiasm for biology, ecology, and environmental sciences, and who are intrigued by quantitative methods, including statistics and ecological modelling.

Biology 17 
Social and environmental risks associated with food sustainability 

Supervisor

Dr Michael Clark

Description

In response to our food system’s contribution to the worsening global ecological and climate crises, organisations are setting targets relating to the impacts of their food consumption on the environment and will therefore need to carry out various actions to reduce their impacts. These could range from ‘top-down’ actions such as reducing the amount of red meat served, to ‘nudge-based’ actions such as placing more sustainable foods at the top of the menu. Different actions are associated with different social risks to the organisation (e.g. consumer discontent due to infringement of choice), and environmental outcome risks/uncertainties (e.g. potential for increasing food waste). Having a systematic way to evaluate and quantify these risks would help organisations choose which strategies to pursue to meet their targets. This project would expand on the research conducted by this research group assessing transition pathways to environmental targets for an organisation’s food-related operations. It will do so by helping incorporate risk assessment of these transition pathways, helping organisations understand trade-offs and potential win-wins between environmental impact reduction and social risks.

Project outcomes

You will evaluate the social risks (to an organisation) and environmental risks associated with switching to more sustainable food behaviours, and will develop a methodology/framework for systematically quantifying these risks. This could include various methods suited to your skills and interests, including:, qualitative research with example organisations (e.g. Oxford college caterers, restaurants, etc.); analysis of transcripts from previous qualitative research to identify social risks; literature reviews to identify environmental and social risks; discrete choice experiments to investigate how individuals might respond to different food interventions (e.g. in canteens or retail stores); and investigating synergies/antergies between different risks. You will write documentation for this methodology/framework, and have an opportunity to write a project synopsis and/or give a presentation on your project results.

Entry requirements

We are looking for proactive applicants that have strong written and oral communication skills. Background knowledge of issues around sustainability, biodiversity and conservation are desirable but not essential.

Biology 18 
ExStream: Multiple Stressors in Rivers 

Supervisor

Dr Michelle Jackson

Description

Rivers are very vulnerable to stressors from multiple sources, yet our knowledge of their combined effects remains limited - an important oversight since stressors can interact to cause unpredictable outcomes that will influence how we conserve and manage natural ecosystems. The goal of this project is to empirically quantify how stressors associated with sewage pollution and climate change interact to impact freshwater communities and ecosystem functioning. You will help us run a large-scale outdoor mesocosm experiment and/or help us process samples from the experiment in the laboratory.

Project outcomes

You will develop valuable field ecology skills, gain experience in general lab work, learn how to identify invertebrates, while also be given the opportunity to get experience with a wide range of novel technologies used in bio-monitoring and environmental science, such as flow imaging microscopy.

Entry requirements

There are no specific entry requirements.

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Clinical Medicine

Clinical Medicine 01
Sero-prevalence of emerging viruses in west Africa

Supervisor

Professor Miles Carroll

Description

Emerging infectious diseases (EID) such as coronaviruses (MERS, SARS-CoV-2) and filoviruses (Ebola and Marburg viruses) have highlighted the potentially devastating health and economic consequences of pathogen spill over from wildlife. Despite the ability to rapidly identify novel pathogens, emerging diseases are frequently only identified once they are widespread in human populations. This project will utilise sera collected from bushmeat hunters and villagers living within the forested regions for west Africa. You will use serological methods such as ELISA, Luminex and western blot to characterise potential immune signatures to emerging viruses. Understanding the sero-prevalence of EIDs will support risk assessments of the likelihood of future spill over events of emerging viruses.

Project Outcomes

Improved understanding of virus spill over risk of emerging viruses

Entry requirements

Knowledge of basic virology and immunology is required. Suitable degree subjects include microbiology / virology, immunology, biomedical sciences or biochemistry.

Clinical Medicine 02
Improving antimicrobial susceptibility testing through the use of high-resolution microscopy

Supervisor

Dr Nicole Stoesser

Description

Bacterial antimicrobial resistance (AMR) is a major health threat. To preserve the efficacy of current antibiotics, there is a need to better manage their use. Mitigation of AMR includes developing faster and more accurate diagnostic tests. Currently patients with infections are started on broad-spectrum antibiotics based on symptoms. Following microbiological investigation in a diagnostic lab, confirmation of the identity of the infecting bacteria enables more targeted antibiotic treatment. At present, complete diagnostic evaluation for common infections generally takes ~2 days. We are developing rapid tests to identify bacteria directly in clinical samples, and determine the correct antibiotics needed for treatment. This combines microfluidics, high-resolution microscopy, and machine learning to diagnose infection in <60 minutes. To do this, we need to understand the cellular responses of bacteria to different antibiotic exposures. You will perform antimicrobial susceptibility testing on clinically relevant bacteria and compare the prediction of resistance against establishment methods.

Project Outcomes

During the project you will work with microbiologists, physicists, and computer scientists, developing skills in microbiology, assay development, fluorescent microscopy, and data analysis. The aim of the project will be to produce a summary scientific report on the findings of your experimental evaluation and present this in one of our research laboratory meetings. Depending on the work undertaken and findings this could also be submitted as a conference abstract +/- contribute to a scientific publication.

Entry requirements

A biomedical sciences background would be helpful.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Clinical Medicine 03
Genetic variation in the hepatitis C virus and its impact on liver disease

Supervisor

Dr Azim Ansari

Description

Next generation sequencing (NGS) promises a high-throughput way to determine genetic materials. The accumulation of enormous sequencing data has led the scientific community to the new discipline of bioinformatics in recent decades. Hepatitis C virus (HCV) is a bloodborne RNA virus transmitted mainly through unsafe injections. After exposure and acute infection, some people will clear spontaneously the virus but a majority will remain infected. During chronic infection, the liver of some people will be damaged by the formation of fibrosis, which can lead to cirrhosis. Cirrhosis is a major cause of morbidity and mortality worldwide and many questions remain unanswered on how the virus affects this outcome. In this project, we will use HCV genomic sequences and bioinformatic approaches to identify if genetic variation in the virus are associated with liver disease outcomes, in particular fibrosis and cirrhosis. A machine learning pipeline will also be introduced to verify the predictability of the identified factors.

Project Outcomes

You will learn the basics of programming and how to handle large-scale sequencing data. You will learn how to perform viral genome wide association studies and predict putative clinical outcomes with the genetic material using machine learning methods especially classification methods.

Entry requirements

You should have an interest in biology, statistics, machine learning and computing. Previous programming experience in bash/Python/MATLAB/R/Java is desirable but not essential.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Clinical Medicine 04
Genome sequencing of hospital sink microbes for antimicrobial resistance surveillance

Supervisor

Dr Nicole Stoesser

Description

Antimicrobial resistance (AMR) poses a significant and growing challenge to healthcare worldwide, with >1.27 million attributable deaths globally per year. Bacteria with AMR genes such as methicillin-resistant Staphylococcus aureus (MRSA) can result in difficult-to-treat infections since these genes confer resistance to antibiotics. Clinical environmental AMR reservoirs such as hospital sink drains have been linked to disease outbreaks and ongoing transmission of drug-resistant infections in hospitals, thus representing tangible targets for intervention and surveillance. This project accompanies a nationwide collaboration (SinkBug) surveying 300+ hospital sinks for pathogens and AMR. You will join an interdisciplinary team comprising molecular microbiologists, bioinformaticians and statisticians to perform whole genome sequencing of bacteria isolated from hospital sinks of interest as identified by SinkBug. This will be a start-to-finish project where you will conduct bacterial culture and identification, DNA extraction and sequencing, and analyse the generated sequence data using bioinformatic tools.

Project Outcomes

You will be trained in aspects of molecular microbiology, biostatistics and bioinformatics, and undertake independent laboratory and computational work. For instance, you will conduct workflows leading to and including Nanopore sequencing. At the end of the project, you will present your findings at an internal group meeting and have the option to contribute to any manuscripts arising from your work as a named co-author.

Entry requirements

Degree in Microbiology or similar. Previous laboratory experience in culture, DNA extraction and sequencing desirable

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Clinical Medicine 05
International health professionals and workforce crisis in the NHS

Supervisor

Dr Attakrit Leckcivilize

Description

The NHS has been suffered from workforce shortages in key professions e.g. nurses and GP for years and the pandemic exacerbated this even further. NHS has to rely on international recruitment as one of the mitigation strategies. However, the recruitment and integration of these international staffs could pose challenges to the local NHS trust, care teams, patients and migrants themselves. Our team plan to liaise with our partners in the local trust to explore issues surrounding their international health professionals through changes and development in the last few years including a recent bilateral agreement on nurse recruitment between the UK and Kenya governments. The exact research question(s) will be tailored to the Trust interest but we expect topics such as integration, well-being and career expectation of these staffs to be high in our agenda.

Project Outcomes

The intern is expected to support our team through reviewing of literature and policy documents at the national and the trust level and help coordinating with local stakeholders. Their work will contribute toward a research paper on the topic and if possible a presentation to key stakeholders in the local Trust and the community.

Entry requirements

There is no specific requirement for this project but a good knowledge on the NHS and some qualitative and literature review skills would be advantageous.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Clinical Medicine 06
Understanding Health policies through quantitative text analysis

Supervisor

Dr Attakrit Leckcivilize

Description

Text analysis using Machine Learning techniques have been used in various fields such as political science and economics. These text analysis tools can help to group the US Presidential Inaugural Addresses based on measures of ‘similarity’ or extract key themes and topics from the speeches, while official statements from central banks can be used to explore policy makers’ sentiment/outlook of the economy. Despite the usage of these tools with eg patients’ records and feedbacks, they have not been employed to study health policy extensively. This project aims to use text analysis tools to explore, extract and visualise key information from key speeches of the health policy makers in the UK over the last 10-20 years. We expect the results from this project to be a proof of concept and feed into a further exploration on this topic across countries and international organisations.

Project Outcomes

We expect the finding from the project to be published as research article(s) in peer-reviewed journal. And if possible, it will be used to support our team's future application for external funding to explore international health policy agenda.

Entry requirements

Good knowledge in programming and Machine Learning is essential and interest in health care issues and policies would be advantageous.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Clinical Medicine 07
Deciphering nuclear division in parasites

Supervisor

Dr Richard Wheeler

Description

Division of the nucleus is an important process for all eukaryotic cells. The nucleus is surrounded by a membrane called the nuclear envelope and in the parasites Trypanosoma and Leishmania the nuclear envelope remains intact during division. In the last steps of nuclear division this membrane must divide, but how this happens is not known. The aim of this project is to understand the steps that occur during nuclear envelope division. We will examine and model our volume electron microscopy datasets enabling us to generate 3D models of dividing nuclei. Moreover, we will examine our library of electron microscopy data to identify nuclei undergoing nuclear envelope division and together this will enable us to define the steps of this process.

Project Outcomes

Detailed understanding of nuclear envelope division. Training in modelling of volume electron microscopy

Entry requirements

Interest in fundamental biological processes

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

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Medicine

Medicine 01
Why does turbulent blood flow cause vessel disease?

Supervisor

Professor Ellie Tzima

Description

Our arteries are exposed to various types of blood flow depending on their shape. When blood flow is turbulent, endothelial cells that line arteries become inflamed and activated, resulting in chronic inflammation and development of plaques. These plaques can obstruct blood flow to the heart or brain and cause heart attacks or strokes. The mechanisms by which endothelial cells sense and respond to turbulent blood flow are a mystery. Work from our group has identified specialised receptors expressed on the surface of cells whose function is to detect blood flow and send signals that ultimately result in disease. One of these receptors is called Plexin D1. We now aim to understand in greater detail the mechanism by which Plexin D1 senses blood flow and how it signals to other cells to form a plaque. The project involves a combination of tissue culture cell-based experiments, molecular biology, and advanced microscopy techniques. The project has the potential of being tailored to suit your research interests and techniques you want to specialize in.

Project Outcomes

The project will give you an opportunity to learn several lab techniques which are highly transferable. These include mammalian cell culture, application of shear stress to cells in culture, western blotting, qPCR, immunofluorescent staining of cells and tissue and confocal imaging. You will have an opportunity to attend weekly lab meetings and present your work in front of the entire group to hone your presentation skills. You will also receive mentorship and guidance if interested in pursuing a PhD or a future career in research.

Entry requirements

It is desirable to have a background or strong research interest in Biochemistry/Cell Biology/Vascular Biology/Physiology.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Medicine 02
Identifying sex-modulated regulatory regions in the genome affecting brain development

Supervisor

Professor David Sims

Description

Many neurological diseases and genetic disorders show clear sex biases in their prevalence, symptoms, severity and prognosis. However, the molecular effects of biological sex on gene expression is often overlooked and understudied. This project will give the candidate the opportunity to use bioinformatic and computational skills to integrate large genomic datasets (RNAseq, ATAC-Seq, ChIP-Seq, chromosome conformation capture, GWAS etc) to identify sex modulated elements involved in brain development and investigate their potential role in neurological disease. This project will employ different computational methods, including machine learning to understand the molecular pathways driving the sex-specific effects at different genomic regions.

Project Outcomes

The candidate will get experience in genomic analysis, programming and data science best practices as well as an in-depth understanding of many different genomic assays with the aim that the project will contribute to a publication.

Entry requirements

This project would be suitable for someone from any science, technology, engineering or mathematics (STEM) background, as long as they are able to program in Python.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Medicine 03
AI deep learning for clinical research

Supervisor

Dr Qiang Zhang

Description

AI deep learning is transforming the world in many aspects, but its real-world clinical applications have been hindered by the knowledge gap between machine learning scientists and clinicians. We actively address this by developing AI algorithms next to clinical doctors at an interdisciplinary clinical research unit. We offer opportunities to work with a cross-disciplinary team to gain experience in developing deep-learning solutions for unmet clinical needs. This may include data pre-processing, neural network design, data analysis, and method validation.

Project Outcomes

You will gain domain knowledge of both deep learning and cardiovascular imaging, skills in medical data processing, neural network design in python, and valuable experience of developing AI algorithms in clinical research settings.

Entry requirements

You should have a background in computer science or engineering. You should have experience in machine learning and coding in Python.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Medicine 04
The function of the Menin protein in leukaemia

Supervisor

Professor Tom Milne

Description

Infant and childhood leukaemia caused by the MLL-AF4 fusion protein is an aggressive disease with few effective treatments. MLL-AF4 binds at gene promoters and activates a transcription program that leads to leukaemic transformation. A promising new drug target for this disease is the chromatin adaptor protein Menin, which interacts with MLL-AF4 at its target genes. Drugs that inhibit Menin and MLL-AF4 interactions are now in early clinical trials, but it is unclear how these inhibitors disrupt the transcription of key cancer genes. Our preliminary proteomics data has identified multiple transcription factors that Menin may interact with to sustain the leukaemia. In this project, we will validate one of these Menin-transcription factor interactions and explore its relevance to cancer gene expression. To accomplish this, will use a range of molecular biology techniques, including chromatin and protein immunoprecipitation, and bioinformatic analysis of sequencing data to investigate the activity of these proteins.

Project Outcomes

By the end of this project, you will have learnt about studying leukaemia biology, and have a good understanding of experimental design, methods for studying gene regulation, and approaches to data analysis and interpretation.

Entry requirements

An interest in experimental (wet lab) work and gene regulation in cancer.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Medicine 05
Precision Therapeutics for Cardiovascular Inflammation

Supervisor

Head of Chemokine Technology Development Graham Davies

Description

Our groups’ interests are in anti-inflammatory proteins from nature, specifically from ticks and viruses. We have identified specific regions of these proteins (16 amino acid peptides) which maintain this anti-inflammatory effect. You will work on these novel peptides to determine their biochemical (e.g. binding to target by Biolayer Interferometry (BLI)) and biological activity (e.g. cell migration assays) and elucidate their potential effect on inflammatory diseases.

Project Outcomes

To learn how to conduct and interpret experiments that will contribute to the groups' work.

Entry requirements

A biological/scientific background would be required.

Medicine 06
Gene Therapy for lung surfactant deficiency

Supervisor

Professor Deborah Gill

Description

In some rare circumstances babies are born at term normally, but are unable to breathe due the lack of lung surfactant. These babies end up on a ventilator with no prospect of treatment except lung transplant. We are using human cells to see if gene therapy could help these babies.

Project Outcomes

This is an opportunity to work alongside a post-graduate student developing new treatments for lung surfactant deficiency; skills include: use of human cell culture disease models; gene delivery to human cells; mammalian cell culture; molecular biology skills; planning, recording and analysis of experiments.

Entry requirements

All skills can be taught

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

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Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS)

NDORMS 01
Patient and public involvement in clinical trial design and reporting

Supervisor

Professor Sally Hopewell

Description

At the Oxford Clinical Trials Research Unit and Centre for Statistics in Medicine, we carry out research into how medical research is designed and reported – and how these things could be done better. This project will focus on patient and public involvement in the design and reporting of randomised controlled trials. Involving patients and the public in how we design and report trials is very important: it helps researchers to target questions that matter, conduct studies in ways that are accessible and acceptable to patients, and communicate findings to the people who they affect. You will analyse a representative sample of clinical trial protocols and reports of trial results. You will look at whether and how the researchers have provided information about involving patients and the public in the design and reporting of their trials. This will help us to understand the current situation and pinpoint where improvements are needed, and will provide a baseline for evaluating future progress.

Project Outcomes

You will develop an understanding of important features of clinical trials, and skills related to reviewing and analysing published trial reports. Your work may result in co-authorship on a publication.

Entry requirements

Suitable degree subjects include Medicine (or other courses related to healthcare), or Mathematics / Statistics (with an interest in clinical research).

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

NDORMS 02
Responsible and open research practices by UK funders

Supervisor

Dr Patricia Logullo

Description

Open science and reproducibility should be valued not only by researchers themselves but also by funders. The organisations providing financial support for research in health in the UK should be requiring that the science produced using money from taxpayers or donations from citizens is open, meaning that it can be scrutinised and reproduced whenever necessary. The same applies to the researchers that funders select to support: how are scientists selected to receive support? We will integrate the UNIQ+ interns in one of our ongoing projects. The interns will get in contact with and understand what responsible research and what open research practices are (definition, options available, how to use them). After discussing with us the basics and understanding what are responsible research practices and how they can be incentivised, the interns will work with a prespecified list of 149 funders, visiting their website and collecting information on their general identification and about how they select or approve the applicants: They will be able to give their opinion on the extraction form, in a small pilot study, so that it can be adjusted. The interns will work in pairs, with “double extraction” and we will mediate conflict resolution meetings. The simple, descriptive analysis planned for this part of the research will be authored by the interns.

Project Outcomes

Raise awareness about responsible and open research practices. Exercise critical thinking.

Entry requirements

No. Just basic literacy and numeracy skills and interest in research.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

NDORMS 03
Reported open science and reproducibility practices in health research publications

Supervisor

Dr Michael Schlussel

Description

Open science and reproducibility practices are now valued more than ever in academic environments. However, it is not clear whether adherence to reporting guidelines, data and analysis-code sharing, statements of authorship contribution, declarations of conflict of interests, type of funding, research pre-registration, and protocol availability, among other research integrity practices, are evenly adopted and reported in published scientific articles. UNIQ+ students will join a project using a meta-research approach (i.e., an appraisal of the content of published articles in peer-reviewed journals) to estimate the prevalence of such practices in a selected sample of publications from different areas of medical sciences. Students will be thought about the importance of such practices and trained to extract data from publications using a short, standardised questionnaire, in duplicate. Any discrepancies will be resolved by consensus. If consensus is not achieved, the opinion of a third senior researcher may be sought.

Project Outcomes

UNIQ+ students will have the opportunity to be authors of any peer-reviewed articles originating from this meta-research. UNIQ+ students will also develop research skills and learn about research integrity and the importance of open science and reproducibility practices.

Entry requirements

There are no specific requirements

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

NDORMS 04
Regulatory network of neutrophil development in health and disease

Supervisor

Professor Irina Udalova

Description

Neutrophils exert anti-microbial activity through several mechanisms including release of cytotoxic products, reactive oxygen species, neutrophil extracellular traps, and pore-forming molecules. The presence of immature neutrophil subsets with abnormal functions are consistently associated with inflammation-driven pathologies, including sepsis, vasculitis, and severe Covid19. The molecular control of pathogenic neutrophil responses is largely unknown. The goal of this project is to generate the regulatory blueprint of neutrophil states during development and in a signal-driven microenvironment. The mapping of the regulatory blueprint will be achieved using multi-scale computational analysis of publicly available and in-house genomics data. Network analysis and machine learning models will be implemented to identify and inform of key transcriptional regulators and neutrophil states crucial to development in homeostasis and inflammation.

Project Outcomes

The outcome of this study is expected to progress fundamental biology of neutrophils, increase our understanding of neutrophil activated subsets in disease and aid the development of new targets for therapeutic interventions in inflammatory disorders.

Entry requirements

Either bioinformatics or statistics/mathematical degree subjects with interest in biological sciences are desirable. Basic knowledge on programming languages such as R, python, and shell would be helpful.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

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Oncology

Oncology 01
Interaction of supporting cells (fibroblasts) and macrophages in cancer

Supervisor

Dr Eileen Parkes

Description

We are interested in understanding how a certain protein on fibroblasts can change their interactions with important immune cells, called macrophages, in cancer. These immune cells can change their behaviour depending on their surroundings, which makes them interesting and important to understand in cancer. You will learn cell culture, immunofluorescence and western blotting during your attachment.

Project Outcomes

You will identify if a fibroblast protein changes macrophage behaviour.

Entry requirements

Suitable degree subjects include biochemistry, molecular biology, life sciences or related subjects.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

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Pathology

Pathology 01
Mechanisms of protein degradation

Supervisor

Professor Pedro Carvalho

Description

Accumulation of misfolded proteins and aberrant protein aggregates are hallmarks of a wide range of pathologies such as neurodegenerative diseases and cancer. Under normal conditions, these potentially toxic protein species are kept at low levels due to a variety of quality control mechanisms that detect and selectively promote their degradation. Our lab investigates these protein quality control processes with a particular focus on ER-associated degradation (ERAD), that looks after membrane and secreted proteins. The ERAD pathway is evolutionarily conserved and in mammals, targets thousands of proteins influencing a wide range of cellular processes, from lipid homeostasis and stress responses to cell signalling and communication. We investigate the mechanisms of ERAD using multidisciplinary approaches both in human and yeast cells. We are using CRISPR-based genome-wide genetic screens and light microscopy experiments to identify and characterize molecular components involved in the degradation of disease-relevant toxic proteins. In parallel, we use biochemical and structural approaches to dissect mechanistically the various steps of the ERAD pathways.

Project Outcomes

It should be possible to characterize a new effector or substrate of the ERAD pathway using biochemical and/or genetic tools.

Entry requirements

You should have a background in biological sciences, biochemistry, physiology or medicine.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Pathology 02
Advanced imaging and image analysis to understand early embryo development

Supervisor

Professor Jordan Raff

Description

Centrosomes are important organisers of the cell and their dysfunction has been linked to a plethora of human diseases. Almost all cells are born with a single centrosome that grows and divides; when the cell divides, each daughter inherits one centrosome and the cycle starts again. This project involves using sophisticated microscopes to make movies of living fly embryos expressing fluorescently-tagged versions of various centrosome proteins. These movies will be analysed using computational methods to track how the centrosomes grow and divide through multiple rounds of division. We ultimately hope that these quantitative measurements will allow us to mathematically describe how centrosome growth and division are regulated during embryo development, providing important insight into how these processes go wrong in disease.

Project Outcomes

You will learn how to handle sophisticated microscope and computer systems to generate and analyse large imaging datasets from living embryos. You will also learn some essential genetic, cell and molecular biology.

Entry requirements

Most relevant for someone studying a subject with an element of biology.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Pathology 03
Biophysical analysis of T cell receptor binding kinetics

Supervisor

Professor Omer Dushek

Description

T cells are important white blood cells that orchestrate immune responses. They can be activated when they recognize the molecular signatures (‘antigens’) of infections. They recognize antigens using their T cell antigen receptors. When this recognition is accurate, it can be helpful leading to the elimination of viruses and bacteria (foreign antigens) but when inaccurate, it can lead to autoimmunity (self antigens) or allergy (innocuous antigens). We now know that the ability of T cells to discriminate between different antigens depends on the binding kinetics between the T cell receptor and the antigen. However, precise measurements of these binding kinetics and especially between the T cell receptor and self antigens are limited. Here, the T cell receptor and antigen will be produced and purified from E.Coli and their binding affinity and kinetics will be measured using a biophysical technique known as surface plasmon resonance. The data will be analysed by fitting mathematical models in order to extract the binding affinity and kinetics for different interactions. The objective will be to determine the difference in affinity between foreign and self antigens.

Project Outcomes

You will gain valuable experience in protein production, a popular biophysical assay for binding, and mathematical modelling. The research findings may be included in a future research study.

Entry requirements

Suitable degree subjects include Molecular biology, Biochemistry, Biophysics, and Biological sciences.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Pathology 04
How cells ensure chromosome inheritance

Supervisor

Professor Fumiko Esashi

Description

In many animals and plants, every chromosome contains a unique structural region, called the centromere. The centromere recruits the kinetochore machinery, which ensures proper segregation of chromosome when cells divide. Curiously, the centromeric DNA sequences are least conserved even between closely related species, but they are commonly composed of arrays of repetitive element in animals and plants. We study why and how these repeats have evolved at centromeres, with specific focus on the mechanism called homologous recombination (HR). HR is an evolutionarily conserved mechanism that catalyses homology-directed repair of DNA breaks and is essential for cell survival. Surprisingly, our recent study has revealed that centromeres harbour unusually high levels of intrinsic DNA breaks even in non-cycling cells, driving hyper-recombination. Building on this observation, the project aims to elucidate how centromeric DNA breaks impact on the fitness of human cells.

Project Outcomes

The student will learn to assess cellular phenotype, genetics and/or advanced imaging, depending on their primary interest, during the project. This will involve cell culture, microscopy and molecular biology. The student will also gain a clear understanding of the research field of genome stability control and centromere biology.

Entry requirements

The project would be suitable for anyone who has backgrounds in molecular and cellular biology, genetics, biology and/or biochemistry.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Pathology 05
ADP-ribosylation signalling in regulation of DNA repair

Supervisor

Professor Ivan Ahel

Description

Poly(ADP-ribose) polymerases (PARPs) are a family of enzymes that synthesise ADP-ribosylation, a reversible modification of proteins that regulates many different cellular processes. Mammalian PARP called PARP1 is known to regulate the DNA damage response by modifying different DNA repair protein factors. Recently we showed that while PARP1 is essential for the DNA damage induced protein ADP-ribosylation, it is not sufficient and requires an accessory factor we named HPF1. Furthermore, we discovered a specific hydrolase (called ARH3) that reverses PARP1/HPF1-dependent modifications. The aim of this project is to provide insight into the mechanism by which PARP1/HPF1 and ARH3 (de)modify their target protein substrates. The project will include mutagenesis of the functional residues in these proteins and their substrates, expressing these mutants as recombinant proteins and testing the efficiency ADP-ribosylation reactions in vitro using purified proteins. Techniques employed will be site-directed mutagenesis, cloning, protein purification and biochemical assays.

Project Outcomes

Learning basic molecular biology and biochemistry techniques. Biochemical characterisation of the recombinant proteins using biochemical assays.

Entry requirements

Suitable degree subjects include courses related to Biological Science or Biomedical Science.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

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Population Health

Population Health 01
Machine Learning Heterogeneity in Population Health

Supervisor

Professor Aiden Doherty

Description

Machine-learning is increasingly used in health-related applications. One example is machine-learning models to derive measurements of behaviours (such as physical activity) from wearable device data. These measurements have been used to study associations between physical activity and health/disease. Often, the model achieving the best internal validation is deployed on a large-scale health dataset. However, even machine learning models with comparable performance, when deployed, might lead to different outcomes in the downstream health association studies. This project aims to investigate how heterogeneity in data curation and model training could change conclusions about how behaviours are associated with health. This project might shed light on caveats one may need to consider when using machine-learned metrics in epidemiologic studies.

Project Outcomes

You will train and deploy various versions of human activity classifiers on the UK Biobank. Then, you will empirically assess how the different measurements obtained impact downstream analyses of the association between physical activity and health outcomes. The results will be written in a technical report with the possibility of conversion into a publication.

Entry requirements

You will be expected to work with Python/R in this project. Some familiarity with deep learning frameworks such as Tensorflow/Pytorch would be helpful. Knowledge of statistical inference would be nice to have. Suitable degree subjects include Computer Science, Engineering, Physics, Maths or a similar quantitative discipline.

Funding information

This internship may be funded by an external partner and may in this case be offered for either seven weeks or for an extended duration of ten weeks with an associated scholarship stipend of £4,200. If you express an interest for this project in the application form, you will be asked to confirm if you can undertake the internship for seven weeks only, or seven weeks or ten weeks.

Population Health 02
Piecewise constant Cox process: applications to meta-analysis of fMRI studies

Supervisor

Professor Thomas Nichols

Description

Meta-analysis is an essential tool to improve statistical power by pooling evidence from multiple studies. While a conventional meta-analysis combines a single measure over studies (e.g. a drug-placebo difference), in the context of functional magnetic resonance imaging (fMRI) each "result" is a set of locations in 3-dimensional brain atlas space. The 3D coordinates are analyzed in a "coordinate-based meta-analysis" (CBMA) where the goal is to identify the locations that consistently arise across different studies. In this project you will compare different methods for conducting CBMA. The simplest methods simply 'blur out' the points, in what is known as a kernel-based method. Bayesian CBMA are more sophisticated and are more interpretable than kernel-based methods. One Bayesian CBMA uses a log-Gaussian Cox Process (LGCP) for meta-regression, which considers the spatial dependence of neuroimaging data and the heterogeneity among studies. This project proposes a potentially more efficient alternative of LGCP with piecewise constant functions as the prior distributions, and posterior distribution will be inferred by reversible-jump MCMC, which provides the underlying intensity function of the Poisson point process. This project aims to investigate the potential improvement in efficiency and scalability over LGCP without sacrificing accuracy and flexibility.

Project Outcomes

You will learn the basic knowledge of meta-analysis of neuroimaging studies. You will also understand the design of both LGCP and BRBP models. Then, you will implement the algorithm of BRBP with application to working memory dataset, and compare the computational efficiency and consistency of activation regions discovered by both algorithms. The results will be written in a technical report with the possibility of conversion into a publication.

Entry requirements

You will be expected to work with Python or R in this project. Some familiarity with Bayesian statistics, Gaussian Process and MCMC would be helpful.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Population Health 03
Ethnic differences in obesity and disease risk

Supervisor

Dr Jennifer Carter

Description

It is well known that obesity increases your risk of heart disease and diabetes, but most of this evidence comes from Caucasian adults in Western societies. We need more evidence from diverse populations. We have detailed data on the amount of fat mass stored across different regions of the body in Malay, Chinese and Indian adults from The Malaysian Cohort. This is the largest ethnic data set to date with such detailed measurements of obesity, with over 4,000 measurements available. Using statistics (regression in R or Stata), there are several possible projects students could choose from. For example:

  1. Do the associations between body fat distribution and blood pressure differ between ethnic groups?
  2. Do the associations between body fat distribution and blood glucose differ between ethnic groups?
  3. Do the prevalence of risk factors for heart disease differ by education, sex and ethnic group?
  4. How does menopause affect fat distribution in different ethnic groups?

Project Outcomes

You will contribute toward work which will be published in a peer-reviewed manuscript.

Entry requirements

An understanding of basic statistics such as means, standard deviations and correlations is required. An understanding of slightly more advanced topics like linear regression is desirable (but not essential). Students with a background in biology, sport or exercise science, medicine and health studies, medicine (general), or anatomy would be suited for our placement.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Population Health 04
Cancer epidemiology

Supervisor

Dr Christiana Kartsonaki

Description

The aim of the project will be to study risk factors or biomarkers for certain types of cancer. The specific objectives can be adapted to match the students' interests and background. The project may involve a systematic review and meta-analysis, or other literature review and/or data analysis. For example, it may be a systematic review and meta-analysis on adiposity and risk of prostate cancer, or on a different risk factor and cancer type. Alternatively it could be on the analysis of a cancer-related dataset. You will learn how to search the literature, use the statistical software R to analyse data, plan research and perhaps write a protocol or analysis plan, and some epidemiological and statistical concepts and methods.

Project Outcomes

It is anticipated that the work may contribute to a paper to be submitted for publication.

Entry requirements

You should have an interest in epidemiology, medicine, health, (bio)statistics, or a related field.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

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Primary Care Health Sciences

Primary Care Health Sciences 01
Cancer epidemiology: Primary care and the cancer diagnostic pathway

Supervisor

Dr Diana Withrow

Description

Electronic medical records from primary care are routinely linked to records from hospitals, the cancer registry, and death registrations and anonymized for research purposes. From this ‘big data’ one can explore what is and is not working efficiently and equitably in the health care system. You will work with the QResearch group, which holds a database including 35 million patients, to explore diagnostic pathways for cancer and their impact on survival. You are likely to contribute to evidence synthesis (literature review) and data analysis and to learn foundations of cancer epidemiology.

Project Outcomes

You will contribute toward work which will be published in a peer-reviewed manuscript

Entry requirements

Some training in statistics is desirable but not essential. Suitable degree subjects include Medicine, Human / Life / Biomedical Sciences, Statistics / Data Science or Pharmacology.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

Primary Care Health Sciences 02
Using patient interviews to test a new typology of access to general practice

Supervisor

Professor Catherine Pope

Description

There is a crisis in general practice and getting (or rather, not getting) access to appointments with a GP is a significant concern for the public, patients, practitioners, service providers and policy makers. Innovative systems and approaches intended to improve access have been studied but results have been inconclusive and contradictory. We are conducting a larger project to learn from what happened in practices that participated in earlier evaluations of different access systems and which subsequently continued, adapted or abandoned these systems. We have developed a model or typology of different ways that people get an appointment to see their GP and want to use a separate database of previously collected interviews with patients to test this typology. The internship will entail:

  1. Reading approx. 100 interview transcripts (c. 30 pages each) from ‘HealthTalk’ studies of patient experiences of COVID 19 cancer; and
  2. Identification of relevant codes already assigned, extraction of excerpts and thematic analysis.

Project Outcomes

Potential skills developed include understanding of coding and thematic approaches to qualitative health research, secondary (text) data analysis, experience of working with the HealthTalk database, knowledge of access to general practice in the UK, report/paper writing, working with a larger project team. You will identify relevant codes and make a collection of extracts from the interview data, and thematically analyse these in conjunction with the typology to produce a report or journal paper or other relevant output to disseminate the learning about access to general practice.

Entry requirements

You need to have some aptitude for or interest in working with text based interview data. The data are coded using the NVIVO software platform and support can be provided to use this but some prior knowledge of this software will be an advantage. Suitable degree subjects include Medicine, Social Sciences (eg Sociology or Anthropology, but also anyone used to dealing with textual data (eg English or History).

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

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Surgical Sciences

Surgical Sciences 01
Investigating the Role of Sentinel Lymph Node Biopsy (SLNB) in Melanoma Skin Cancer

Supervisor

Dr George Adigbli

Description

Sentinel lymph node biopsy (SLNB) is a pivotal prognostic marker in melanoma. By identifying patients with subclinical lymph node metastasis, it guides management decisions for patients with advanced disease. Large multicentre trials, which indicated that SLNB followed by completion lymph node dissection confers no survival value provoked wide discussion about the utility of SLNB. Current opinion suggests SLNB should be regarded solely as a staging procedure, however emerging evidence questions whether this notion overlooks the immunological properties of the lymph node.

Project Outcomes

You will analyse clinical records in our melanoma database to investigate the relationships between SLNB, melanoma recurrence and response to immunotherapy. You will develop skills in data collection and statistical analysis, and understanding of emerging concepts in surgical onco-immunology.

Entry requirements

A degree in medicine/biomedical science/life sciences and/or computer science.

Funding information

This internship may be funded as a Wellcome Biomedical Vacation Scholarship (BVS). The benefits of a Wellcome BVS placement are the same as those for UNIQ+ but you will be employed by the University and paid a basic salary at real Living Wage plus holiday pay and National Insurance contributions (estimated to be in the region of £3,351 before tax and employee National Insurance). Please refer to the What is a UNIQ+ Research Internship? page and the eligibility requirements for further details about Wellcome BVS placements.

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