Oxford skyline with the UNIQ plus logo
A view of the Oxford skyline. (Image credit: Elizabeth Nyikos / Graduate Photography Competition)

UNIQ+ (2022)

About

UNIQ+ research internships are designed to provide students from under-represented and disadvantaged backgrounds who are ordinarily resident in the UK, with the opportunity to experience postgraduate study. The information on this page is for our 2022 programme.

UNIQ+ aims to provide you with a real day-to-day experience of postgraduate research. During the six-week programme, which will run from Monday 4 July 2022, you will undertake a research project, attend training skills sessions and receive information on graduate study. You will meet and work with our researchers, academic staff, and graduate students.

UNIQ+ will give you the chance to experience some of what Oxford offers its students. Our intention is that everyone who takes part will gain benefits in terms of confidence, skills and experience that will enhance both their CV and any future postgraduate applications. During UNIQ+, you will live in college accommodation and experience life as a graduate student in Oxford. Social activities, including some organised lunches and dinners, will introduce you to our community and to some of the University of Oxford’s famous traditions and locations.

We intend to offer around 100 UNIQ+ internships to individuals who meet the eligibility criteria (which can be found in the Eligibility criteria section of this page). We encourage applications from talented individuals who would find continuing into postgraduate study a challenge for reasons other than academic ability.

The deadline for applications is 12:00 midday UK time on Friday 18 February 2022.

What happens during a UNIQ+ internship?

You will receive an induction at the start of UNIQ+, which will introduce you to the University, the programme and the other participants.

In the first week of the programme, a range of courses may be offered to participants depending on which training (if any) is most appropriate for your research project. This could include training on both quantitative and qualitative data analysis. The quantitative analysis element may include two separate courses on an introduction to data analysis using each of the Python and R languages. Additionally, there are likely to be a range of research skills courses available including an introduction to bibliographic management and an introduction to version control using Git and Github. Detailed information on first week training will be shared with successful applicants in June 2022.

Your induction will be followed by a research project that will take place in a department within our Medical Sciences, Mathematical, Physical and Life Sciences (MPLS), Humanities, or Social Sciences Division. During the six weeks of the internship, you will be expected to undertake research full-time in Oxford. You can find out more about the projects that are available in the Projects section of this page.

Over the course of your project you will have many opportunities to apply and develop your research skills and gain real-life research experience. While working on your project, you will receive regular supervision from an academic member of staff, post-doctoral staff and/or current DPhil (PhD) students. You will also receive training in transferable skills (eg presentation skills, how to prepare a CV), and information on how to make a competitive application for graduate study.

At the end of the programme, you will write a report on your project and give a short presentation to other participants of our Graduate Access Programmes.

We will seek to deliver this programme in accordance with the description set out in this page. However, there may be situations in which it is desirable or necessary for the University to make changes, either before or after the start of the programme. The safety of students, staff and visitors is paramount and major changes to delivery or services may have to be made in circumstances of a pandemic (including Covid-19), epidemic or local health emergency.

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.

Opportunities and benefits of UNIQ+ internships

UNIQ+ is a paid research internship. You will receive:

  • a scholarship stipend of £2,500 for the six-week programme (due to take place 4 July to 12 August) designed to offset any loss of the opportunity to take up paid employment during the summer (different arrangement are in place for ESRC-funded placements in Social Sciences - please see below);
  • free-of-charge accommodation provided by one of our colleges (more information about accommodation can be found below);
  • up to £250 to cover your travel expenses to and from Oxford at the start and end of the programme; and
  • an application fee waiver for applying to a graduate course at Oxford (currently the application fee is £75 per application).

UNIQ+ is designed to:

  • enhance your research skills;
  • enhance your ability to make a competitive application to postgraduate courses;
  • introduce you to leading researchers and staff at the University of Oxford; and
  • offer you information about opportunities for postgraduate study and research careers.

UNIQ+ DeepMind internships (Ten-week projects)

Up to 12 UNIQ+ DeepMind research internships in artificial intelligence and machine learning are available this summer to individuals from disadvantaged and underrepresented backgrounds. These internships are funded by DeepMind, one of the leading industry innovators in these fields.

UNIQ+ DeepMind internships have the same benefits as our UNIQ+ internships, including free-of-charge accommodation, but will take place over ten weeks with an associated scholarship stipend of £4,200.

UNIQ+ projects which may be offered as either six-week UNIQ+ internships or ten-week UNIQ+ DeepMind internships are highlighted in the Mathematical, Physical and Life Sciences (MPLS) section of the ‘Projects’ tab.

The application process and eligibility criteria are the same as for other UNIQ+ internships and you will need to apply by the deadline of 18 February 2022.

ESRC-funded placements in Social Sciences

We intend to offer up to six ESRC placements to individuals who meet the UNIQ+ eligibility criteria (which can be found in the Eligibility criteria section of this this page).

In addition to meeting the UNIQ+ eligibility criteria, to be considered for a placement with ESRC funding, you will need to be in the middle years of your first-degree studies and expected to obtain at least an upper second class (2:1) UK honours degree. These placements can only be undertaken in projects in the social sciences that are eligible for ESRC funding, which 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 £2,600 before tax and employee National Insurance).

Accommodation

You will be offered a single room in accommodation provided by one of our colleges (see our FAQ: What is a college?) for the duration of the programme at no cost to you. We will let you know which college will provide accommodation to you if you are offered a place.

You will be able to check in to your room on Monday 4 July, at the start of the programme and you will need to check out on Saturday 13 August.

UNIQ+ supporters

The UNIQ+ programme is supported by, and has been able to expand thanks to a generous donation by Sir Michael Moritz and Ms Harriet Heyman, who also fund the Crankstart Scholarship programme for prospective undergraduate students.

The programme is also supported by:

  • DeepMind
  • EPA Cephalosporin Fund;
  • ESRC;
  • Oxford British Heart Foundation Centre of Research Excellence (Oxford BHF CRE);
  • participating colleges that will offer free-of-charge or subsidised accommodation to the programme and make their facilities available to their accommodated participants; and
  • participating departments, faculties, graduate training programmes and institutes within the University of Oxford’s Humanities Division, Mathematical, Physical and Life Sciences Division, Medical Sciences Division and Social Sciences Division, including:
    • BBSRC Oxford Interdisciplinary Bioscience Doctoral Training Partnership
    • EPSRC- and MRC-funded Sustainable Approaches to Biomedical Science (SABS) Centre for Doctoral Training.

Projects for entry in 2022

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

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 the same subject area, which means that in the majority of cases we would expect all three projects to be offered by the same academic division, eg you select three humanities projects.

Many of our projects are open to those studying undergraduate degrees in a broad range of subjects, however you should read each project description carefully and check to see if it has any specific entry requirements before applying. Full instructions for completing the application form can be found our Application Guide for Graduate Access Programmes.

Places for UNIQ+ internships will be distributed across the four academic divisions, with 15 places expected to be available for projects in the Humanities and 25 to 30 places available for projects in each of the remaining divisions (Social Sciences, Medical Sciences, and Mathematical, Physical and Life Sciences).

Only projects that are matched to successful applicants will run this year. If you are successful, we will try to match your interests to available projects and supervisors. 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.

Humanities projects

Hum 1: English 
Transfers of power in early modern England and Europe

Supervisor

Professor Paulina Kewes, Professor of English Literature, Faculty of English Language and Literature

Description

This project will introduce humanities students to the digital research skills necessary to investigate how the early moderns construed the role of parliaments in determining the royal succession. You will develop an understanding of how representative bodies such as the English parliament or the Polish Sejm debated and shaped regime change. Training will encompass core techniques in mining digital repositories (such as the Royal Historical Society Bibliography), Critical Discourse Analysis (CDA), and building datasets. You will then apply these skills by writing an essay on how early modern MPs, polemicists, political thinkers and/or imaginative writers conceptualised the power of parliaments to decide who would be king.

Project outcomes

The goal of this project is to familiarise students with cross-disciplinary and comparative research. You will each produce your own dataset of terms, phrases, and broader 'parliamentary' discourse associated with early modern royal successions. These datasets will become part of the more encompassing database of the project 'Recovering Europe's Parliamentary Culture, 1500-1700', which will be hosted on the project's website (parliamentaryculture.web.ox.ac.uk; live from January 2022). You will also use your own datasets to write an essay or report on the 'parliamentary' way of dealing with early modern transfers of monarchical authority in a specific country. Depending on your language skills, this will either be a scholarship review or an empirical study. All work will be credited in full on the project website.

Entry requirements

There are no entry requirements for this internship: the case studies and focus will be tailored to accommodate and expand your disciplinary background and linguistic competence; we welcome applicants with foreign language skills but this is not a condition.

Hum 2: English 
Fake news before social media, 1649-1660

Supervisor

Professor Paulina Kewes, Professor of English Literature, Faculty of English Language and Literature

Description

This project will introduce humanities students to the digital research skills necessary to investigate the cultural impact of newsbooks, newspapers, and other printed materials produced during the budding media landscape of early modern Europe. Focusing on Britain's revolutionary moment (1649-1660), you will learn how to use digital tools to investigate the role of news media in the spread of ideas throughout Europe. Training will encompass core techniques in mining digital repositories, critical discourse analysis (CDA), and building datasets. You will then bring these skills into practice by writing an essay about the consonant/conflicting ways in which news outlets in different European regions reported on the English parliament during its brief period of republican rule and/or how English media presented parliamentary culture(s) abroad.

Project outcomes

The goal of this project is to familiarise students with cross-disciplinary research. You will produce your own dataset of seventeenth-century terms, phrases, and broader discourse associated with parliamentary culture in one or more European languages. These datasets will become a part of the more encompassing database of the project 'Recovering Europe's Parliamentary Culture, 1500-1700', which will be hosted on the project's website (parliamentaryculture.web.ox.ac.uk; live from January 2022). You will also use your own dataset to write an essay or report on what you think characterised the international discourse on seventeenth-century parliamentary culture. All work will be credited in full on the project website.

Entry requirements

There are no entry requirements for this internship: the case studies and research focus will be tailored to accommodate and expand the students' own disciplinary backgrounds

Hum 3: Classics 
Documenting cultural heritage

Supervisor

Dr Miranda Williams, Research Associate, Faculty of Classics

Description

The Manar al-Athar Photo-Archive (www.manar-al-athar.ox.ac.uk) 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 undertake the preparation of a set of photographic data relating to an archaeological site(s) for publication on the Manar al-Athar website. You will be involved in the digital editing of photographs to publication standard; undertaking library-based research to ensure the accurate labelling of photographs (including the identification of appropriate bibliography); preparing English-language labels for photographs based on a standardised hierarchical labelling system; either liaising with other project members to provide Arabic-language labels for photographs or, if you have some background in Arabic, preparing the Arabic-language labels; and preparing geo-data (GPS coordinates and uniform resource identifiers) for embedding within the EXIF metadata of the photographic files.

Project outcomes

The project will result in the preparation of a complete set of photographic data relating to an archaeological site(s)  and its uploading to the Manar al-Athar Photo-Archive where it will be freely available for users under a Creative Commons 2.0 licence. You will also prepare a set of social media posts relating to the archaeological site on which your work focussed, and a short (approx. 3 mins) YouTube video, using Camtasia software, utilising the photographs on which you worked and your background research. During the project, you will be an integral part of the Manar al-Athar project team. You 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 social media posts and short video, you will have the opportunity to practice communicating academic research to a non-specialist audience, and to contribute to Manar al-Athar wider impact.

Entry requirements

There are no specific entry requirements. An interest in the archaeology, history and cultural heritage of the Middle East and/or North Africa is desirable. The project may be of particular interest if you are undertaking a degree in archaeology, classics, history or oriental studies, although those studying other subjects are welcome to apply.

Hum 4: History 
Education and activism: women in Oxford

Supervisor

Professor Senia Paseta, Professor of Modern History, Faculty of History

Description

2020 marked the centenary of the formal admission of women to the University of Oxford. From October 1920, women could matriculate and therefore take degrees for the first time, despite having studied at the University since the late 1870s. In recognition of this milestone, a team representing the Faculty of History, the Bodleian Libraries, the former women’s colleges (Lady Margaret Hall, Somerville, St Anne’s, St Hilda’s and St Hugh’s) and the Oxford Martin School Programme on Women’s Equality and Inequality, has created Education and Activism: Women at Oxford University, 1878-1920, a new research project and online resource. This collaborative project commemorates the centenary and contributes to research on women, education and political activism.

Project outcomes

You will undertake further research for the project in college libraries and possibly the Bodleian Library, identifying potential collections for inclusion on the website, and writing a short article or report on either a collection or specific source which you have identified. You will also be involved in communications work for the website as well as helping to add new information to it. You will work with your supervisor with input from college librarians to engage with a number of the tasks involved in creating and maintaining a research website. Your report/article will be published on the website.

Entry requirements

Experience in history from your undergraduate degree is desirable but not essential. We would also welcome other humanities applicants with an interest in women's history.

Hum 5: History with Theology 
Early modern libraries (the Allestree Library)

Supervisor

Lead: Dr Sarah Mortimer, Associate Professor of Early Modern History, Faculty of History

Additional: Dr Sarah Apetrei, Faculty of Theology and Religion

Description

The Allestree library in Christ Church College remains mysterious. Many books are annotated, some have indications of ownership and provenance, and others contain loose manuscript notes. The aim of the project will be to study a section of the library, describe the important features of the books, and reflect on what this tells us about the 17th century intellectual life. You will have an opportunity to work with early modern books, improve your palaeography skills, and learn about cataloguing. You will also be expected to engage in some independent research about the benefactors to the library and their intellectual interests. The project is imagined as interdisciplinary between the faculties of history and theology & religion, as the collection contains many works of theology.

Project outcomes

You will produce a report on your findings, both a text version which may be written up for a library journal or the Christ Church College library newsletter, and a short podcast or videocast for the Christ Church College website. Your work will also contribute to the cataloguing of the library and our understanding of its collection.

Entry requirements

You should have experience in a relevant humanities discipline (eg history, religion, theology) from your undergraduate degree. Some experience with early modern handwriting would be desirable but not essential.

Hum 6: Philosophy 
Responsible AI

Supervisor

Dr Maximilian Kiener, Leverhulme Early Career Fellow, Research Associate, University College Oxford, Faculty of Philosophy

Description

Artificial Intelligence (AI) increasingly executes tasks that previously only humans could do, and even outperforms humans. Yet, the generally most beneficial AI also tends to be the least controllable (its processes are too fast to be monitored in real time) and the least transparent (its technological bases, eg deep neural networks, create ‘black boxes’ with impenetrably complex algorithms). This project investigates those AI systems in specific case studies and asks how the use of AI may undermine traditional conditions of responsibility, leading to a so-called responsibility gap.

Project outcomes

In the first phase (weeks 1-2) you will conduct online research to formulate a succinct ethical question concerning the use of AI in a particular domain. In the second phase (weeks 2-5) you will develop a solution to the problem you have identified, drawing on multiple perspectives and disciplines, consulting with stakeholders if appropriate, presenting and discussing your ideas with others, and producing a report, essay or policy recommendation. You will produce one or more case studies on the use of AI and a literature review on the challenges of AI related to responsibility and accountability.

Entry requirements

There are no specific entry requirements.

Hum 7: Medieval and Modern Languages  
Global Kafka

Supervisor

Professor Barry Murnane, Associate Professor in German, Faculty of Medieval and Modern Languages

Description

Global Kafka is a research initiative building up to the centenary of Kafka's death in 2024, for which the Oxford Kafka Research Centre will be organising an exhibition in the Weston Library in collaboration with the Bodleian's exhibition team, and partly funded by a John Fell grant. You will engage in a focused research projects relating (1) to Kafka's manuscripts held in the Bodleian and (2) related to the international reception of Kafka's works, based on holdings of author's collections held by the Bodleian. You will work alongside the Directors of the Oxford Kafka Research Centre and the project's post-doctoral coordinator to develop independent micro-projects which will contribute to the final exhibition design and display.

Project outcomes

You will receive training in key bibliographical and research methodologies, including doing literature reviews. You will also work with manuscripts and to navigate the special holdings of the library. You will write short essays or blog posts which will be published on the Oxford Kafka Research Centre website, which is currently being redesigned.

Entry requirements

You should have experience in (comparative) languages and literatures from your undergraduate degree. Knowledge of German would be desirable but not essential. Applicants should be at least in their penultimate year.

Mathematical, physical, engineering and life sciences (MPLS) projects

MPLS 1: Chemistry 
Inorganic chemistry for future manufacturing

Supervisor

Professor Jose M. Goicoechea (Coordinator), Professor of Chemistry, Department of Chemistry

Description

You 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 sustainable chemical synthesis. You 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

You will learn how to safely handle reactive compounds including materials that are air- and moisture-sensitive and analyse chemical compounds using state-of-the-art techniques (including, for example, nuclear magnetic resonance (NMR) spectroscopy, X-ray diffraction, mass-spectrometry, electrochemical methods). You will also receive training in interpreting experimental data, writing scientific reports and presenting your research to an academic audience.

Entry requirements

You should have experience in chemistry, or a chemistry-related subject, from your undergraduate degree. 

MPLS 2: Computer Science 
Double descent and deep learning

Supervisor

Lead: Dr Seth Flaxman, Tutorial Fellow in Computer Science, Department of Computer Science

Additional: Professor Samir Bhatt (Imperial College London/University of Copenhagen), Dr Swapnil Mishra (Imperial College London)

Description

Deep learning often relies on networks with millions of parameters, exceeding the number of observed data points. A flurry of recent research activity in machine learning has surrounded the phenomenon of double descent. In this project we will aim to study how the generalisation accuracy of the solutions of a system varies as the number of parameters increases. Using linear programming, we will investigate different inverses corresponding to varying minimum norms, with the hope of shedding more light on the phenomenon of double descent.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

Using linear programming, you will investigate different inverses corresponding to varying minimum norms with the hope of shedding more light on the phenomenon of double descent. You will then produce a technical report based on your findings which would be posted on arXiv (https://arxiv.org). Your report will form the basis of a computer science conference submission.

Entry requirements

There are no specific entry requirements.

MPLS 3: Biology 
Transmission of variants of concern

Supervisor

Dr Moritz Kraemer, Branco Weiss Fellow, Department of Zoology

Description

Transmission of infectious diseases is highly heterogeneous in space and time. Recent pandemic work has often neglected transmission heterogeneities leading to imprecise and often conflicting policy responses. This project aims to improve our understanding of transmission by reviewing the literature on spatial dynamics of SARS-CoV-2 variants and collate data from different countries and contexts.

Project outcomes

You will work together with collaborators in Oxford and abroad. Further, the project is expected to contribute to a peer-reviewed publication.

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.

MPLS 4: Engineering 
Optimal policies in general-sum games

Supervisor

Professor Jakob Foerster, Associate Professor, Department of Engineering Sciences

Description

In single agent reinforcement learning, the most desirable solution for an agent to find is one that achieves the maximum possible reward. However, in games with multiple agents that each have their own objectives, it is unclear what the most desirable outcome is. The simplest games that explore ideas are social dilemmas such as the Prisoner’s Dilemma, or the Ultimatum Game. In these games, there are multiple possible outcomes that result in different payoffs for the agents. In most previous work, such as work done by DeepMind, it is largely assumed that equitable outcomes also result in the largest sum of rewards; however, this is not true in all games.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

The aim of this project is to see if there is a way to classify and identify desirable outcomes in these games and develop a learning algorithm to discover them.

Entry requirements

There are no specific entry requirements.

MPLS 5: Engineering 
Machine learning for urban dynamics

Supervisor

Associate Professor Xiaowen Dong, Associate Professor of Engineering Science, Department of Engineering Science

Description

According to the United Nations, two thirds of the population in the world could live in cities by 2050. The great concentration of human capital in confined physical space promotes idea exchange and provides opportunities for rapid development of human society, but also leads to massive challenges in urban environment and service management with traditional solutions struggle to cope. At the same time, the recent availability of the huge amount of quantitative data enables the analysis of human behaviour, decision making and societal changes at unprecedented scales and may in particular provide innovative solutions to pressing urban challenges. In this project you will study urban dynamics from a data-driven perspective by combining traditional census-based data with information obtained from social media platforms, and by combining machine learning and network science techniques.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

You will analyse real-world data collected in urban environments and read the literature in urban dynamics via data-driven approaches. You will then implement simple network-based algorithms (such as community detection) in analysing real-world urban data. You will use your findings to try to provide insights into urban dynamics using machine learning and network science techniques.

Entry requirements

There are no specific entry requirements.

MPLS 6: Engineering 
Deep learning, distribution shift and Causality

Supervisor

Professor Philip Torr, Professor of Engineering Science, Department of Engineering Science

Description

The field of Causality offers an elegant theoretical framework to reason about distribution shift, but its combination with traditional machine learning and deep learning has produced disappointing results. While deep learning models have been shown to achieve state-of-the-art performance in several complex visual tasks, these results are often brittle and heavily rely on the assumption that the test set is sampled from the same distribution of the training set. This project aims to analyse in detail the adversarial robustness and data-shift properties of existing methods in order to develop novel insights and understand why Expected Risk Minimisation (ERM) results to be such a strong baseline. These insights will hopefully suggest novel ways of integrating Causality in standard machine learning pipelines.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

You will be working with postgraduate (DPhil) students in a group to work towards a conference or workshop paper.

Entry requirements

You should have the ability to code and have studied a first year maths foundation course.

MPLS 7: Engineering 
Chaos in multi-agent learning

Supervisor

Professor Jakob Foerster, Associate Professor, Department of Engineering Science

Description

Multi-agent learning in many-player games is frequently complicated by learning pathologies. As a result, agents may not converge stably to beneficial Nash equilibria. In this project, you will investigate how chaos arises in the learning dynamics across a number of deep multi-agent reinforcement learning approaches and environments. You will then proceed to investigate techniques aiming to shape or stabilise multi-agent learning, and whether and how those techniques mitigate the ensuing chaos in the learning dynamics.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

You will further understand how chaos arises within the learning dynamics of multi-agent systems, and how to mitigate it. We hope to identify both novel approaches to chaos mitigation, as well as novel analysis techniques for chaos in the learning dynamics of many-player games.

Entry requirements

Prior experience in coding, especially Python, and strong analytical skills are essential.

MPLS 8: Engineering  
"Tournament structure" and social welfare

Supervisor

Professor Jakob Foerster, Associate Professor, Department of Engineering Science

Description

Understanding policies that do well in general-sum settings is an active area of research. At a high level, there are two different approaches: spanning reinforcement learning and evolutionary methods. This project addresses tournament design for state-of-the-art general-sum learning algorithms, and the evolution over tournament structures to maximise the social welfare of the winning policy (or other interesting characteristics).

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

The project includes coming up with a representation for tournaments that can be used for evolutionary algorithms. A second part entails evolving over the representation and comparing state-of-the-art general-sum learning algorithms in the emerging tournament designs. We are interested in the effect of tournament design on the performance of general-sum learning paradigms such as arrogance, opponent-shaping and consistency.

Entry requirements

Experience in materials science, engineering or physics and basic knowledge of deformation and microstructure from your undergraduate degree is desirable but not essential.

MPLS 9: Engineering 
Intravascular ultrasound device

Supervisor

Associate Professor James Kwan, Associate Professor of Engineering Science, Department of Engineering Science

Description

Peripheral artery disease (PAD) is the narrowing of blood vessels and causes serious pain, reduced mobility, ulceration, and limb amputation. Current therapies have limited success due to re-occlusion of the vessels. To prevent re-occlusion, drugs are added to either the stent or the balloon used in angioplasty but with little success. Thus, there remains a surprisingly growing population that present with restenosis leading to amputation. Ultrasound may be able to deliver and implant sound-responsive drug-loaded particles into the artery for the treatment of PAD. Once delivered, these particles reside in the artery, slowly and locally releasing a well-known anti-restenotic agent. Therefore, this project aims to develop and characterise a bespoke drug delivery ultrasound catheter device.

Project outcomes

You will work on an acoustic characterisation profile of an intravascular ultrasound device. The work conducted here will continue the ongoing development of a prototype device.

Entry requirements

You should have experience in engineering or physics from your undergraduate degree. Some experience in acoustics is desirable but not essential.

MPLS 10: Zoology 
Food-related environmental impact and sustainability

Supervisor

Lead: Professor E.J. Milner-Gulland , Tasso Leventis Professor of Biodiversity, Director of the Interdisciplinary Centre for Conservation Science, Department of Zoology

Additional: Dr Michael Clark, Department of Zoology; Eleanor Hammond, Department of Zoology

Description

In response to our food system’s contribution to the worsening global ecological and climate crisis, researchers at Oxford are developing an ‘eco-metric’ tool that enables users to measure their food-related environmental impacts explore options to reduce them to meet environmental targets, and track progress over time. One potential application of this tool is communicating food’s environmental impacts in canteens, eg through ecolabelling. To support these themes, this research project aims to:

  • Engage with eco-metric tool end-users (large caterer, small café, individual consumer) to understand barriers to using the tool, and potential ways to incentivise its use.
  • Engage with caterers to understand barriers to on-site ecolabelling, and ways to overcome them.

Project outcomes

You will participate in various stages of the project including qualitative and/or quantitative research with end-users, data analysis, literature searching or producing materials to aid in their research (eg to help explain incentivisation strategies). You will then produce a written report and present your research findings. If relevant, your research material used during this project would contribute to these ongoing research projects.

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.

MPLS 11: Zoology 
Impact of heat waves and pollution on river ecosystems

Supervisor

Lead: Dr Michelle Jackson, Associate Professor of Freshwater/Marine Ecology, Department of Zoology

Additional: Dr James Orr, Department of Zoology

Description

A large scale field experiment with 128 experimental units will be run in Spring 2022 to explore the impacts of heat waves and pollution on river ecosystems. Many samples will be collected during and after the experiment to describe the responses of the bacteria, algae and macroinvertebrate communities to these multiple stressors. You will primarily assist with the analysis of the macroinvertebrate samples and will develop strong skills in species identification and trait measurement, which are very valuable and sought-after in environmental impact assessment and monitoring.

Project outcomes

You will work with a team of researchers to analyse bacteria and algae samples collected during the field experiment. You will develop valuable skills in lab work and species identification and will be given the opportunity to get experience with a wide range of novel technologies used in biomonitoring and environmental science, such as DNA metabarcoding and flow imaging microscopy. You will also be given the opportunity to collaborate with the group on the research papers stemming from this project.

Entry requirements

There are no specific entry requirements.

MPLS 12: Zoology 
Antibiotic resistance in respiratory microbiome

Supervisor

Dr Rachel Wheatley, George Grosvenor Freeman Fellow by Examination, Department of Zoology

Description

Microbial pathogens are quite often studied in single culture experiments. However, this likely does not represent the microbial communities they may share niche space with during the progression of real infections. You will investigate the antibiotic resistance evolution of a single pathogen species in the presence or absence of respiratory microbiome members. This project will employ microbiological techniques of culturing bacteria (in single or pair-wise combination), sterile working technique, and carrying out antibiotic resistance assays.

Project outcomes

This project is focused around the themes of microbiology and evolutionary biology in biomedical science. You will learn core experimental skills associated with the project. The outcome of this project will be an analysis of how the antibiotic resistance of Pseudomonas aeruginosa changes in the presence of members of a respiratory microbiome.

Entry requirements

There are no specific entry requirements.

MPLS 13: Zoology 
How insects respond to temperature change

Supervisor

Dr Anna C Vinton, NSF Postdoctoral Fellow, Department of Zoology

Description

Understanding how populations adapt to environmental change in order to avoid extinction is a necessary and urgent challenge. Although there have been attempts to make predictions, ecological forecasting remains difficult. Rapid environmental changes have been repeatedly shown to accelerate evolution, or heritable change across generations. Evolutionary rescue in particular occurs when environmental change causes a population to decline, and then the population is able to rebound via adaptive genetic change. You will use a the model fruit fly to investigate how species can adapt to different types of temperature change. This includes how flies vary in their reproductive success, survival, and body size and wing length. This will help to elucidate the different types of responses insects can have to climate change, and serve as a potential model for other species.

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

There are no specific entry requirements.

MPLS 14: Zoology 
Social behaviour in microbes

Supervisor

Professor Ashleigh Griffin, Professor of Evolutionary Biology, Department of Zoology

Description

Our main research interest concerns social interactions between bacterial cells and the implications of these for infection. Antibiotic resistance, for example, can involve cells protecting one another by producing enzymes that break up antibiotic drugs in the environment. To understand how social behaviour, such as antibiotic resistance, evolves over time, we hold extensive collections of clinical isolates from hospital patients: Pseudomonas aeruginosa from the lung infections of patients with cystic fibrosis and Staph. aureus from nasal carriage studies.

Project outcomes

You will have the chance to gain experience working alongside members of our lab on a specific project. Experiments will involve standard microbiological lab techniques - growing bacteria in liquid culture, plating onto agar and using our specialized equipment.

Entry requirements

There are no specific entry requirements.

MPLS 15: Zoology 
Sensing

Supervisor

Dr Ellie Bath, Departmental Lecturer in Animal Behaviour, Department of Zoology

Description

Although most research has focused on male-male aggression, females also fight with each other, with important effects on fitness. However, we know little about how and why females fight. This project will use the fruit fly Drosophila melanogaster to investigate what senses females use in detecting rivals and during fights with other females. Using transgenic fly lines – lines with genes for particular sensory receptors silenced or upregulated to produce flies with one or more senses disabled (eg hearing, smell, touch). This behavioural ecology project will use transgenic lines and machine learning analytic software to achieve its aim of discovering which senses are important for female aggression in fruit flies.

Project outcomes

The project aims to investigate one (or more sense) that are involved in female fruit fly aggression. Depending on the results and the progress of the experiments, the findings in the project will form part of a peer-reviewed publication, with you being a co-author.

Entry requirements

There are no specific entry requirements.

MPLS 16: Zoology 
Signatures of selection on cheating

Supervisor

Dr Laurence Belcher, Postdoctoral Researcher, Department of Zoology

Description

Many infecting pathogens rely on social traits to survive and thrive. 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. In this project you will learn the bioinformatics tools that allow us to analyse DNA sequence data, and the molecular genetics tools that allow us to detect the traces of cheating that are left in genomes.

Project outcomes

The main aim is to assess how cheating evolves in pathogens, with the goal of informing research into the likely success of possible Trojan horse approaches in evolutionary medicine.

Entry requirements

There are no specific entry requirements.

MPLS 17: Zoology 
Species diversity

Supervisor

Dr Stephanie Brittain, Postdoctoral Researcher, Department of Zoology

Description

It is widely accepted in global environmental policy that lands of indigenous peoples and local communities play a significant role in biodiversity conservation. However, support for indigenous and community-led conservation remains largely ad-hoc, local in scale, and insecure. In order to address this situation, evidence is needed on the conditions that enable successful conservation outcomes indigenous or community held lands. This project explores the enabling conditions reported as important for improving or maintaining species diversity on indigenous or community held lands, and what evidence exists for this. You will carry out a literature review and use nVivo to code the enabling conditions identified in each paper, and the evidence provided for this. You may also administer an online survey to understand which of the conditions are considered to be most important, and conduct key informant interviews to explore what barriers may exist to scaling up these conditions.

Project outcomes

This work will contribute to a wider project examining the conditions that are required for a variety of different conservation outcomes on indigenous and community held lands. You will produce a short report on the project results, a presentation to the ICCS lab group and a blog for the ICCS website about your work. If you are interested, you could also be supported to prepare a peer-reviewed paper.

Entry requirements

Prior experience of carrying out literature reviews and an interest in biodiversity conversation are desirable but not essential.

MPLS 18: Earth Sciences 
Fragments of another world: meteorites

Supervisor

Dr Richard Palin, Associate Professor of Petrology, Department of Earth Sciences

Description

You will examine the geological characteristics of meteorites sourced from the large, differentiated asteroid 4 Vesta. This body is often referred to as a protoplanet, and records planetary formation processes in the early solar system that have been lost from the Earth’s geological record. Under the supervision of an experienced team of supervisors, you will combine petrography (e.g. optical microscopy) with quantitative mineral composition analyses (e.g. using SEM/EPMA) to determine the petrology of rock fragments in polymict breccias – or “howardites” – ejected from the surface of 4 Vesta. This classification will be used to determine the degree of crustal evolution and reworking on 4 Vesta, specifically interrogating recent hypotheses that the planetesimal formed a tertiary crust, akin to continents on Earth.

Project outcomes

You will write a short report documenting your findings, with assistance from the supervisor, and will be mentored in how to prepare your scientific results for publication in high-profile, international journals. You will also have the chance to meet with other members of the supervisors' research groups and practise science communication by virtue of informal presentation of your work to your peers.

Entry requirements

There are no specific entry requirements. An interest or experience in geoscience or chemistry would be desirable but not essential.

MPLS 19: Earth Sciences 
Large explosive volcanic eruptions

Supervisor

Professor David Pyle, Professor of Earth Sciences, Department of Earth Sciences

Description

In the past 50 years, a number of explosive eruptions have deposited volcanic ash across large areas of land and sea. In each case, contemporary news stories focussed on the potential for disruption to livelihoods, and damage to infrastructure and the environment. For some events, the physical impacts were assessed at the time; in others, later studies have focussed on possible causal links between the eruption, and identified consequences, or impacts. In very few cases, though, has there been any systematic retrospective analysis of these impacts. The project will use the process of systematic review to survey the peer-reviewed literature, and identify the short- to medium-term consequences of one or more large explosive eruptions.

Project outcomes

You will produce a short report detailing the outcome of the systematic review, and drawing some conclusions about what is, and what is not, known about the consequences of explosive volcanic eruptions for those regions affected directly by volcanic ash.

Entry requirements

You should have experience in earth sciences or physical geography from your undergraduate degree.

MPLS 20: Materials 
Titanium aero-engines

Supervisor

Professor Angus J Wilkinson, Joint Head of Department, Professor of Materials, Department of Materials

Description

For Ti alloys used in fan blades on the entry section of engines sustained hold periods during peaks of applied load-unload cycles can generate dramatic decreases in fatigue lifetime. This “cold dwell” failure mode is complicated and poorly understood and was implicated in the Air France Flight 66 incident (September 2017), and could be behind the United Airlines 328 incident (2021). The Oxford Micromechanics Group have been working on the science underpinning cold dwell fatigue in Ti alloys. Ti alloys for fan blades have a predominantly hexagonal crystal structure causing the deformation response at the grain level to be highly directional. The project will use digital image correlation to study the time dependent plasticity at a microstructural level correlated with EBSD characterisation of the microstructure to identify ‘hot spots’ in the deformation. We will try to capture results in simulations if time and team size permit.

Project outcomes

You will receive training in mechanical testing, SEM and EBSD analysis, metallography, digital image correlation and image analysis and the use of Matlab. You will collect data and begin analysis at a level suitable for a journal publication. You then will present results to the Oxford Micromechanics Group.

Entry requirements

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

MPLS 21: Materials 
Photoluminescence imaging

Supervisor

Dr Sebastian Bonilla, Associate Professor of Materials, Royal Academy of Engineering Research Fellow, Department of Materials

Description

Photoluminescence (PL) imaging is an important tool for characterising solar cells. It can detect defects in solar cell materials that are not observable to the eye, which is similar, in principle, to the way that x-ray imaging can detect broken bones. However, commercially available PL imaging systems are very expensive. Control of the light intensity of the illumination source is critical, as different defects in solar cells may only be visible at certain illumination intensities. We have developed a laboratory PL imaging tool using inexpensive components; however, careful calibration is required to understand the relationship between the voltage, light intensity and operating carrier density in the solar material. This project will focus on developing an understanding of this relationship.

Project outcomes

You will be a part of the research team to develop a calibrated photoluminescence imaging tool.

Entry requirements

You should have experience in electronic engineering or physics from an undergraduate degree.

MPLS 22: Materials 
Joints for high temperature superconducting wires

Supervisor

Lead: Professor Susie Speller, Professor of Materials Science, Department of Materials

Additional: Professor Chris Grovenor, Department of Materials

Description

The large magnets required for applications in medical MRI and large physics experiments like the LHC at CERN are all based on superconductors and contain numerous joints that are often the (very expensive) points of failure. You will work on the materials science aspects of making and testing joints in high temperature superconductor wires and tapes – how to improve reliability and performance, and understanding what goes wrong. The project will involve designing joint making processes and performing microstructural observations to check phase purity and distributions in the joints as well as testing joint properties.

Project outcomes

You will gain experience in a materials laboratory on processing, characterisation and testing superconducting properties.

Entry requirements

You should have experience in materials or physics from your undergraduate degree.

MPLS 23: Maths 
Randomised algorithms in machine learning

Supervisor

Dr Benjamin Fehrman, Research Fellow, Mathematical Institute

Description

Machine learning has found diverse applications in artificial intelligence technologies, such as the development of voice and image recognition to automated vehicles. Such techniques provide a systematic way of isolating patterns in high-dimensional data sets through the training of artificial neural networks. However, due to the sheer size and scope of modern data, it is often infeasible to train the network with respect to the entire data set at once. Instead we optimize at each step over random samples of the data, which leads to randomized algorithms in machine learning. The most common of these is stochastic gradient descent (PDF).

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

The purpose of this project will be to provide a twofold introduction to randomized algorithms in machine learning. You will develop the probabilistic background necessary for the analysis of such algorithms, beginning by analysing stochastic gradient in simple, convex settings, and to show optimal rates for its convergence. You will then discuss the loss landscape in deep learning and be introduced to algorithms that are designed to treat the degeneracies in the loss landscape. This mathematical analysis will be the primary focus of the project. You will also gain experience implementing real machine learning algorithms. You will learn how to compute over and optimize artificial neural networks, which will give a better appreciation for the mathematical difficulties inherent to machine learning. Indeed, there remains no complete theoretical explanation for the extraordinary success of machine learning techniques in practice.

Entry requirements

There are no specific entry requirements. Experience and an interest in mathematics and programming are desirable but not essential.

MPLS 24: Maths 
Generalized twisted Alexander polynomials for fiber knots

Supervisor

Dr Bin Sun, Postdoctoral Research Associate, Mathematical Institute

Description

The twisted Alexander polynomial, obtained by twisting the Alexander polynomial by finite dimensional representations of the knot group over commutative fields, is a useful knot invariant. For example, Kirk and Livingston used the twisted Alexander polynomial to prove that the knot 8_17 is not concordant with its inverse. Recently, Henneke and Kielak introduced the notion of an agrarian torsion for fiber knots, which can be interpreted as the Alexander polynomial twisted by representations over (not necessarily commutative) division rings. The aim of this project is to construct new knot invariant using the notation of an agrarian torsion. We will carefully choose the twisted representation so that the resulting agrarian torsion can distinguish pairs of knots that share the same classical invariants such as the Alexander polynomial, Jones polynomial, etc.

Project outcomes

The first goal of this project is to find a pair of fibered knots that share the same classical invariants but nevertheless can be distinguished by the agrarian torsion for some representation over division rings. The ultimate goal of this project is to find a way to choose division ring representations for a large class of fibered knots such that the resulting agrarian torsion serves as a powerful invariants for those knots.

Entry requirements

You should have a basic understanding of knots, fundamental group of knot complements, and Reidemeister torsion.

MPLS 25: Maths 
Deterministic simulation of random systems

Please note, the previous title of this project was 'Simulation of stochastic systems'. No other changes have been made.

Supervisor

Dr James Foster, Postdoctoral Research Associate, Mathematical Institute

Description

Stochastic differential equations (SDEs) are popular models for time-evolving random systems. They have seen widespread applications in mathematical finance, molecular dynamics and machine learning. Monte Carlo simulation is usually employed for SDEs – which can require large numbers of samples to be accurate. Tree-based algorithms provide an attractive alternative as they are deterministic and thus avoid the inaccuracies of random sampling. However, since trees grow exponentially quickly, it becomes necessary to control the number of particles representing the SDE solution. For example, one approach inspired by modern filtering algorithms is to group particles together using a “k-d tree” or related algorithm and replace large groups of particles with smaller collections of “sigma points” (obtained using a Cholesky decomposition).

Project outcomes

You will learn about stochastic differential equations and investigate algorithms for their numerical approximation. The project will likely focus on a “real-world” SDE using your preferred programming language so that you can get a flavour of what mathematics research could be like. You will also be investigating both deterministic and stochastic methodologies.

Entry requirements

You should be familiar with topics in mathematical analysis. Experience in probability theory and/or numerical analysis would also be helpful.

MPLS 26: Maths 
The curve shortening flow

Supervisor

Professor Jason Lotay, Professor of Pure Mathematics, Mathematical Institute

Description

A key problem in geometry, with its origins in antiquity, is to find the shortest curves with some properties, like bounding a fixed area in the plane. One way to tackle problems like this is to use the curve shortening flow, which is a mechanism for evolving a curve and trying to make it shorter as quickly as possible. Many things are now known about this flow but there are also many interesting open problems. The aim of this project is to look at some of the basic examples of the curve shortening flow, some of its applications, and some of the key issues still to be resolved.

Project outcomes

You will produce a written report on the basics of curve shortening flow, some of the major breakthroughs in the field, some important applications and open questions.

Entry requirements

You should have experience in mathematics or physics, with a theoretical physics emphasis, from your undergraduate degree. Knowledge of geometry, topology and/or partial differential equations would be useful but not essential.

MPLS 27: Maths 
Finding large cap sets

Supervisor

Dr Thomas Bloom, Research Fellow, Mathematical Institute

Description

A cap set is a set without any three points in a line. An old question in combinatorics asks how large such a set can be found inside a finite-dimensional vector space over a finite field. The search for such bounds uses a wide range of techniques from finite geometry, combinatorics, analysis, and number theory. Recently a breakthrough was made in the search for upper bounds, using only undergraduate linear algebra. This project would focus on complementing this by improving our understanding of lower bounds, which have not been improved since 2004.

Project outcomes

You will use a blend of explicit computation to find some explicit large cap sets and theoretical advances, to understand how to make such computations more efficiently, and how small cap sets can be glued together to find larger cap sets. You will use techniques from combinatorics, geometry, or linear algebra. The goal is to reach an explicit improvement of the current known lower bounds for the cap set problem. This may be from a single large example found experimentally, or new theoretical ideas that lead to an asymptotic improvement.

Entry requirements

You should have experience in mathematics (or a joint honours mathematics degree) from your undergraduate degree. Basic programming skills would be helpful.

MPLS 28: Maths 
Modelling and simulation of crowds

Supervisor

Dr Rafael Bailo, Postdoctoral Research Associate, Mathematical Institute

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. 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.

MPLS 29: Physics 
Mapping the vorticity field

Supervisor

Dr Carlos Garcia-Garcia, Beecroft Fellow, Department of Physics / Astrophysics

Description

We want to map the projected vorticity field and compute its angular correlation function. This field is only sourced by non-linearities in the standard cosmological model, and will probably have a low signal. However, it is interesting to know if we can detect it with this technique. In this project, you will have to match gravitationally linked pairs of galaxies, and estimate their angular momentum using their redshift difference, which will be mainly driven by their proper motion. We will use the public spectroscopic galaxy catalogues of BOSS DR12 or the eBOSS LRG. Once the angular momentum field is constructed, you will have to project it onto the celestial sphere, compute its correlation function, and compare its result with theoretical expectations for cosmic vorticity.

Project outcomes

You will be using coding languages, mainly in Python, and different codes of interest in cosmology and data analysis. Your work will be a part of and lead to the publication of a paper.

Entry requirements

You should have a background in physics or statistics.

MPLS 30: Physics 
Machine learning for serendipitous discovery

Supervisor

Professor Chris Lintott, Professor of Astrophysics and Citizen Science Lead, Department of Physics / Astrophysics

Description

This project uses modern machine learning techniques to try and find unusual objects in large astronomical datasets. Machine learning is good at identifying anomalies, but less good at identifying which of these unusual members of a class are worthy of further investigation and which are merely artefacts. This project will test the potential of deep learning techniques and a citizen science project hosted via the Zooniverse platform to contribute to this problem, using the representation of the dataset developed in training the machine learning return to decide which should be reviewed by volunteers. Example datasets which could be used include deep galaxy surveys such as DECals, or using data from NASA's planet hunting satellites Kepler and TESS. If time allows, there will be time to follow up anything found using other archives and making new observations.

Project outcomes

You will lead the preparation of a short moderated note for the Research Notes of the American Astronomical Society, providing a published record of your work. You will also present to the Zooniverse collaboration on the results of the project.

Entry requirements

Experience of coding would be helpful, as well as a background in physics, computer science or another similar subject.

MPLS 31: Physics 
Computational analysis of the RNA sequence-structure map

Supervisor

Miss Nora Martin , Postdoctoral Research Assistant, Department of Physics / Rudolf Peierls Centre for Theoretical Physics

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 languages, especially Python would be desirable but not essential.

MPLS 32: Physics 
Atmospheric trace gases

Supervisor

Dr Anu Dudhia, University Research Lecturer, Department of Physics / Atmospheric, Oceanic and Planetary Physics

Description

The IASI satellite instruments view downwards, measuring the infrared spectrum emitted by the earth. Using these spectra, we have developed algorithms to detect the presence of a number of gases with low concentrations. The project will be to analyse these results, decide if the results are plausible, identify anomalies.

Project outcomes

You will produce a brief report describing the dataset, comparison with other measurements or models, and identification of anomalies with suggested improvements.

Entry requirements

You should have experience in a STEM subject and computer programming knowledge in Python.

MPLS 33: Plant Sciences 
Plants under pressure

Supervisor

Professor Gail Preston, Director, Oxford Interdisciplinary Bioscience DTP, Department of Plant Sciences

Description

In the field plants face multiple stresses, from living sources such as pests and pathogens (“biotic stress”), and also from non-living sources related to soil conditions or changing weather conditions (“abiotic stress”). Some plant species, such as the crop plant Chenopodium quinoa (quinoa), are well-adapted to grow in challenging environments (saline, arid) that would impose a high level of abiotic stress on many other plant species. Our previous work suggests that plant species that are adapted to either normal soils (e.g. Arabidopsis thaliana) or saline conditions (e.g. quinoa) differ in their immune responses when exposed to pathogens. This could be related to different types or varying expression of immune-related genes as a consequence of distinctly different evolutionary pressures. In this project you will use biochemical and cell biology approaches to compare the immune responses of different plant species and use computational methods to compare the immune-related genes present in their genomes.

Project outcomes

You will generate novel data that will provide new insight into how plant adaptations to the environment affect their immune responses, and will gain knowledge and experience in plant biology, microbiology, biochemistry, cell biology and bioinformatics.

Entry requirements

You should have a scientific background.

MPLS 34: Plant Sciences 
Plant cell wall remodelling for drought resistance

Supervisor

Dr Madeleine Seale, Leverhulme Early Career Fellow, Department of Plant Sciences

Description

Cell walls form an important structural basis and a barrier to the outside world for plant tissues. Drought can cause profound changes to cell shape and structure as cells contract and cell walls collapse. Cell walls are also normally highly hydrated maintaining their shape and flexibility. However, cell walls can be dynamically restructured and altered in response to the environment. The moss, Physcomitrium patens, is drought tolerant and a number of cell wall remodelling genes are upregulated in response to drought.

Project outcomes

You will analyse knockout mutants of cell wall re-modellers and characterise cell wall composition and material properties in response to drought. You will use genetic analysis and microscopy to establish responses to drought. You will also make use of newly developed microviscosity probes to assess the physical properties of cell walls during drought. The project will help to understand land plant strategies to cope with a desiccating environment.

Entry requirements

There are no specific entry requirements.

MPLS 35: Plant Sciences 
Submergence depth perception in plants

Supervisor

Dr Francesco Licausi, Associate Professor of Plant Sciences, Department of Plant Sciences

Description

Full submergence negatively affects plant fitness since it inhibits gas exchange and thus hinders both photosynthesis and respiration. Some plants, including rice varieties, avoid this stress by rapid internodal elongation that enables at least part of the plant body to emerge out of the water surface, thus restoring contact with the gas atmosphere. The aim of this project is to test whether plants that mount such adaptive response modulate it according to the depth of the water column by ‘measuring’ the pressure that is exerted on leaves (and other organs). This project will involve plant growth, quantitation of elongation under submergence, mutant genotyping, and possibly gene expression analyses.

Project outcomes

The proposed activity will provide an answer to whether plants modulate submergence-induced growth not only based on their reserves but also on the likelihood to restore contact with the atmosphere. You will learn to grow plants (in vitro and in soil) for research, to extract DNA for mutation analysis, and to measure gene expression via real time qPCR.

Entry requirements

There are no specific entry requirements.

MPLS 37: Statistics 
Machine learning and AI for SARS-CoV-2

Supervisor

Professor Garrett Morris, Associate Professor of Systems Approaches to Biomedicine, Deputy Director of Graduate Studies, Department of Statistics

Description

You will investigate the application of machine learning and/or deep learning to the problem of designing small molecules to inhibit emerging drug targets in SARS-CoV-2, in particular its main protease (Mpro) and papain-like protease (PLpro). You will explore the use of Python, cheminformatics (RDKit) and a variety of data science and ML libraries, including Pandas, SciKit-Learn and AutoGluon. The aim is to produce machine learning-based models that predict binding affinity of ligands for proteins, and you will test them on SARS-CoV-2 targets. Both regression and classification models can be explored.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

You will produce machine learning-based models that predict binding affinity of ligands for proteins, and test them on SARS-CoV-2 targets.

Entry requirements

Ideally you should have a background (or interest in) chemistry or biochemistry. If you have a background in mathematics, statistics, computers science or physics, please refer to the PDB 101 website, in particular the section on Health and Disease, and Coronavirus Proteases. Familiarity with programming in Python would be preferable.

MPLS 38: Statistics 
Post-hoc accuracy measures for variational inference

Supervisor

Lead: Dr Jun Yang, Florence Nightingale Bicentennial Fellow and Tutor, Department of Statistics

Additional: Professor Yee Whye Teh, Department of Statistics

Description

Many modern applications of machine learning involve such massive datasets that sampling schemes such as many traditional MCMC methods have become impractical. In order to allow the use of Bayesian approaches with these big datasets, it is actually much faster to compute variational approximations of the posterior by using optimization algorithms. Variational inference has indeed become a cornerstone algorithm for fast Bayesian inference. Recently, the development of rigorous theory for variational inference becomes a very active research topic. This includes a priori guarantees, such as consistency and posterior concentration of variational inference, and a posteriori guarantees, such as post-hoc accuracy measures and diagnostic tools. This project aims to develop theoretical tools for analysing the performance of approximate Bayesian methods used in modern machine learning. The goal of the project is to develop new transportation inequalities and use them as new post-hoc accuracy measures for validating the approximate accuracy of variational inference.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

You will introduce to Bayesian machine learning and learning theory. You will also produce technical report or workshop paper.

Entry requirements

You should ideally have a background in applied maths, and be familiar with probability and optimisation.

MPLS 39: Statistics 
Theory of reinforcement learning with low dimensional structures

Supervisor

Professor Patrick Rebeschini, Associate Professor of Statistics, Department of Statistics

Description

Reinforcement learning is a fundamental paradigm in sequential decision making, which has led to a tremendous empirical performance in a variety of applications. Prompted by the challenge to bridge the gap between theory and practice over the past few years increased interest has been devoted to laying rigorous theoretical foundations for this discipline. In particular, the growing emphasis on high-dimensional models has fuelled the development of algorithms that can exploit low-dimensional structures, eg sparsity and low-rankness, to achieve quasi-optimal (typically worse-case) error rates with respect to notions of effective dimensions, eg via the framework of natural policy gradient and mirror descent. This project aims to overview the state-of-the-art of applications of mirror descent in this literature and to explore the design of new algorithmic principles that can better capture low-dimensional structure, going beyond the worst-case analysis done in previous literature.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

You will do literature review and design and analysis of nove algorithmic principles, mostly from a theoretical point of view but possibly also via empirical simulations if of your interest.

Entry requirements

We welcome applicants with an interest in probability theory and optimisation.

MPLS 40: Statistics 
Selection bias with continuous variables

Supervisor

Professor Robin Evans, Associate Professor in Statistics, Department of Statistics

Description

Causal inference is difficult for many reasons, one of which is the presence of selection bias. It has been shown that in the case of discrete variables, if there is some structure in the original distribution this can be used to recover the distribution that was selected upon. The discrete method involves linear algebra, and this project would attempt to use integral equations to extend the method to continuous variables. You will mostly be working on coding up the solutions to the integral equations.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

Your research may lead to a conference publication on the method.

Entry requirements

You should have studied a highly numerate subject and have programming experience.

MPLS 41: Statistics 
Policy-based Bayesian experimental design

Supervisor

Dr Tom Rainforth, Florence Nightingale Bicentennial Fellow and Tutor, Department of Statistics

Description

From psychological trials and virtual assistants to drug discovery and market research - the design of experiments is a fundamental challenge throughout almost all of science and industry. While one-off design problems can often be dealt with effectively by simply hand-crafting designs, many experimental settings require adaptive design strategies to be effective. This project will investigate a new and exciting approach to conducting adaptive experimentation: Deep Adaptive Design (DAD). DAD is a model-based approach that uses ideas from Bayesian experimental design to allow adaptive experiments to be run both quickly and effectively, by learning a deep design policy network that automatically makes design decisions given previous observations. The project will combine ideas from deep learning, variational inference, reinforcement learning, Monte Carlo methods, and active learning.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

It is hoped that the project will lead to a successful new application of the DAD approach to a real-world problem, or a methodological advancement in how it is applied.

Entry requirements

You should be familiar with Python with basic knowledge of deep learning and methods for Bayesian inference (eg Monte Carlo or variational inference). Experience with either PyTorch or Tensorflow is recommended. Experience with experimental design and/or active learning is desirable but not essential.

MPLS 42: Statistics 
Training dynamics of variational inference in Bayesian neural networks

Supervisor

Professor Yee Whye Teh, Professor of Statistical Machine Learning, Department of Statistics

Description

Bayesian neural networks are a popular framework for learning neural networks that take into account uncertainties. The goal of this project is to compare the predictive performances of established variational inference methods for Bayesian neural networks on a set of benchmarking tasks using appropriate metrics, and to investigate the training dynamics of different methods and how they might be related to differences in performance. This project would allow you to reproduce more carefully empirical phenomena observed in passing in prior work and to contribute to the understanding of variational inference in Bayesian neural networks. As part of the project, you would build on an existing codebase and implement your own experiment routines. You will work closely with a senior graduate student in the group (Tim Rudner) and participate in day-to-day interactions with the wider research group.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

You will learn about Bayesian machine learning and deep learning, particularly about variational inference methods for Bayesian neural networks. You will also learn Python and JAX, a functional programming library used in machine learning. You will implement metrics for analysing the training dynamics of variational inference in Bayesian neural networks and analyse and create visualizations of findings. You will then write a report about your research findings.

Entry requirements

You should have experience in a relevant degree such as computer science, statistics, mathematics or engineering. You should have a good understanding of calculus, linear algebra, introductory statistics, and introductory probability theory. You should have studied at least one course on machine learning and have experience coding in Python.

MPLS 43: Statistics 
Bayesian behavioural policies

Supervisor

Professor Yee Whye Teh, Professor of Statistical Machine Learning, Department of Statistics

Description

The goal of this project is to implement different variational inference methods for Bayesian neural networks and to use them as behavioural policies in KL-regularized reinforcement learning. The project would allow you to familiarise yourselves with the basics of reinforcement learning and behavioural cloning, and to build on cutting-edge research on making reinforcement learning algorithms more data-efficient. As part of the project, you would build on an existing codebase and implement your own experiment routines. You will work closely with a senior graduate students in the group (Tim Rudner and Cong Lu) and participate in day-to-day interactions with the wider research group.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

You will learn about Bayesian machine learning, deep learning and reinforcement learning, particularly about variational inference methods for Bayesian neural networks and deep reinforcement learning You will also learn Python and understand implementations of variational inference methods and reinforcement learning algorithms. You will implement training routines for reinforcement learning and analyse and create visualizations of findings. You will then write a report about your research findings.

Entry requirements

You should have experience in a relevant degree such as computer science, statistics, mathematics or engineering. You should have a good understanding of calculus, linear algebra, introductory statistics, and introductory probability theory. You should have studied at least one course on machine learning and have experience coding in Python.

MPLS 44: Statistics 
Equivariant neural network architecture design

Supervisor

Professor Yee Whye Teh, Professor of Statistical Machine Learning, Department of Statistics

Description

Equivariant neural networks (ENNs) have recently become popular by allowing practitioners to easily incorporate symmetries present in data into neural network architectures. These networks are built on linear maps that satisfy a symmetry constraint. Several successful approaches have been proposed to numerically or analytically solve for this constraint, most of them leveraging tools from representation theory. Analogous to the number of channels in typical CNNs or hidden layer size in MLPs, several types of representations can be chosen for the hidden feature maps. However, there is a lack of thorough understanding of the implications of different representation choices and the impacts on performance. Similarly, there is little comparative study of the numerous proposed equivariance-specific activation functions. This project aims to empirically and theoretically study the implications of these choices for learning performance. You will work closely with a senior graduate student and a postdoc in the group (Michael Hutchinson, Emile Mathieu) and participate in day-to-day interactions with the wider research group.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

You will learn about deep learning and equivariant neural networks and existing code-base / libraries, and implementation practicalities. You will empirically assess the performance of several ENNs on standard benchmarks such as rotated MNIST, 3D Tetris shapes and molecular datasets. You will also investigate best practices regarding the choices of representations and activation functions. You may also look at comparative performance with other methods of designing ENNs, eg PDO-based approaches. You will then write a technical report / workshop paper explaining the empirical results.

Entry requirements

You should have experience from a relevant degree in computer science, statistics, mathematics or engineering. You should have a good understanding of calculus, linear algebra, introductory statistics, and introductory probability theory. You should have studied at least one course on machine learning and have experience coding in Python. Desirable: A good understanding of calculus, linear algebra, representation theory, and (Lie) group theory. Well versed on core machine learning models and concepts. Strong experience coding in Python and Pytorch / Tensorflow / Jax, bash, but also experience with running machine learning experiments on a cluster

MPLS 45: Statistics 
Improving machine learning

Supervisor

Professor Chris Holmes, Professor of Biostatistics in Genomics, Department of Statistics

Description

The aim is to explore, and produce a scientific report on, the potential use of unlabelled data to improve machine learning (ML) classification on labelled data. We will consider a classification task such as medical diagnosis where we have access to class labelled training data, eg medical images with associated patient diagnoses, in addition to unlabelled data sets, eg medical images without diagnoses. The task is to explore whether classification accuracy of machine learning can be improved by utilising unlabelled data (which is often more abundant than labelled data) alongside the labelled data.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

You will try different modifications of the suggested approach in the project such as only augmenting with data observations that the teacher model predicts confidently, or giving the predicted data less weight than other observations. You will then produce a report into the use of data augmentation and self-training in AI.

Entry requirements

You should have a background in statistics, mathematics or computer science. Basic understanding of machine learning would be an advantage but not essential.

MPLS 46: Statistics 
Drug-design pipeline integration

Supervisor

Dr Ruben Sanchez Garcia, Turing Fellow, Department of Statistics

Description

The main goal of the project will be to integrate some of our drug discovery tools such as DeLinker (https://github.com/oxpig/DeLinker) into a workflow management program that will allow us to run them within complex pipelines. As a result, you will be able to run automatic drug discovery pipelines with no prior knowledge required.

Project outcomes

You will be a part of the team to develop new methods that will be integrated into our software pipeline package, making them widely available.

Entry requirements

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

MPLS 47: Plant Sciences 
Phylogenetic studies - Ipomoea

Supervisor

Professor Robert Scotland, Professor of Systematic Botany, Department of Plant Sciences

Description

The Scotland group at the University of Oxford studies the taxonomy and evolution of the plant genus Ipomoea, commonly known as morning glories. This genus includes the sweet potato, a species widely consumed, and other species of interest as food, ornamental or medicine. As part of our studies during the last years, we have extracted and analysed DNA from thousands of herbarium specimens. Our studies, however, are not complete, and a large number of samples are still pending to be processed and analysed. The project offers the opportunity to participate in the ongoing study of the genus Ipomoea. You will participate in all stages of a phylogenetic study, from DNA extraction and amplification to computer-based analysis and data interpretation. During your internship, students will work alongside members of the group to learn the basic procedures of lab and computer work, and will receive constant support and supervision. You will extract DNA from leaf material, amplify two DNA markers widely used in phylogenetic analyses, and prepare the DNA for sequencing. Subsequently, you will process the data and incorporate them to already-existing phylogenies of the genus, and will participate in the interpretation and discussion of the results.

Project outcomes

You will work with samples from species of Ipomoea that have never been sequenced, and therefore your contribution will constitute a tangible, important outcome to the study of this group of plants.

Entry requirements

There are no specific entry requirements.

MPLS 49: Computer Science 
Gaussian Process likelihood surfaces

Supervisor

Lead: Dr Seth Flaxman, Tutorial Fellow in Computer Science, Department of Computer Science

Additional: Dr Xenia Miscouridou (Department of Computer Science), Professor Samir Bhatt (Imperial College London / University of Copenhagen)

Description

Gaussian processes are a workhorse of machine learning. A popular approach to fitting GPs is to optimise the log marginal likelihood, which is available in closed form. In this project you will study the curvature of the log marginal likelihood surface to try to understand why Gaussian processes can sometimes perform poorly.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

You will try to understand why Gaussian processes can sometimes perform poorly and use your findings to try to improve optimisation strategies.

Entry requirements

There are no specific entry requirements.

MPLS 50: Engineering Science 
Probing the Focussing Control Mechanism of the Human Eye

Supervisor

Dr Karen Hampson, Researcher, Department of Engineering Science

Description

Adaptive optics is a technique in which the optical component, such as a lens or mirror, adapts its shape to keep an object in focus. The technique was first used in astronomy to correct for blur owing to atmospheric turbulence and has since been applied to retinal imaging and microscopy to achieve unprecedented resolution. The human eye is a biological adaptive optics system. When looking at objects at different distances the lens rapidly and automatically adapts its shape to ensure that the image remains in focus on the retina. There are many unanswered questions about how the eye uses information in the image to do this. The goal of this project is to apply mathematical techniques used in artificial adaptive optics systems to data from the human eye to determine how the eye stays in focus.

Project outcomes

You will gain skills in data collection, data analysis, and presenting research work both orally and in the form of a journal publication.

Entry requirements

You should have experience from a relevant degree, such as in physics, mathematics or computing. Some experience of Matlab is desirable.

MPLS 51: Engineering Science 
Effect of Fractals on the Human Eye

Supervisor

Dr Karen Hampson, Researcher, Department of Engineering Science

Description

It has long been known that viewing fractals with a particular fractal dimension can have a physiological soothing effect. However comparatively little is known about how fractals impact the visual system. When the eye is fixating on a static object, the power of the lens and direction of gaze is constantly fluctuating on a minute scale in order to provide the eye with feedback. The properties of these fluctuations are impacted by stress to the visual system. The goal of this project is to determine the effect of fractal dimension on the focussing control mechanism of the eye and eye movements. You will write software to generate fractals that participants will view while collecting data on focus fluctuations and eye movements. These signals will be analysed using chaos theory analysis, which is a sensitive measure of stress in physiological systems.

Project outcomes

You will gain skills in data collection, data analysis, and presenting research work both orally and in the form of a journal publication.

Entry requirements

You should have experience from a relevant degree, such as in physics, mathematics or computing. Some experience of Matlab is desirable.

MSD 12: Information Engineering 
Bioacoustic detection and mosquito species classification

Supervisor

Professor Stephen Roberts, Royal Academy of Engineering, Man Group Chair in Machine Learning, Department of Engineering Science

Description

There are over 100 genera of mosquito in the world containing over 3,500 species and they are found on every continent except Antarctica. Only one genus (Anopheles) contains species capable of transmitting the parasites responsible for human malaria. Anopheles contains over 475 formally recognized species, of which approximately 75 are vectors of human malaria, and around 40 are considered truly dangerous. These 40 species are inadvertently responsible for more human deaths than any other creature. This project utilizes audio mosquito flight tone data collected with a wide range of well-populated species of wild captured mosquitoes at IHI Tanzania. This dataset consists of the audio recordings of the 8 most populated mosquito species and the task is to perform efficient and accurate mosquito species classification. You will explore classification with unequal class distributions and consider the different degrees of reliability in mosquito detection that are found across species, building these into machine learning models to increase the performance of existing classifiers.

If you wish to select this project for UNIQ+, please choose Medical Sciences on the application form and then select MSD 12: Information Engineering.

This project may be offered as a UNIQ+ DeepMind internship, with an extended duration of ten weeks and an associated scholarship stipend of £4,200. If you express an interest for this project but are unable to undertake a longer, ten-week internship, please indicate this clearly in your personal statement.

Project outcomes

You will learn methods to robustly handle unequal class distributions and improve model accuracy.

Entry requirements

You should have a background in mathematics, physics, computer science or engineering and an interest in real-world machine learning.

Medical sciences projects

MSD 1: Big Data Institute 
Schistosomiasis in Uganda

Supervisor

Lead: Associate Professor Goylette Chami , Robertson Fellow, Research Group Leader, Big Data Institute; Nuffield Department of Population Health

Additional: Dr Max Eyre, Big Data Institute; Nuffield Department of Population Health

Description

Schistosomiasis is a debilitating set of conditions, caused by parasitic blood flukes. Over 700 million people are at risk and most of these individuals live in sub-Saharan Africa. Transmission occurs with human contact and contamination of open freshwater sites. Infections persist because individuals do not have sufficient access to safe water or adequate sanitation. Together with the Uganda Ministry of Health, we are tracking individuals in 36 rural villages of Uganda to understand the factors that relate to their likelihood of getting infected. For schistosomiasis, infection must occur at contaminated water sites where there are competent intermediate snail hosts. This project collects information on all the possible transmission sites in the 36 study villages. The characteristics of these water sites and their suitability for transmission vary widely and depend on the ecological conditions of the site including depth, turbidity, vegetation, and other factors. This project is an exciting opportunity for you to get health data science experience and contribute to a project of global health importance. This project will use either R, QGIS, or ArcGIS to conduct spatial analyses where environmental features from satellite imagery and on-the-ground surveys will be used to construct features related to risk factors for schistosomiasis.

Project outcomes

You will produce modified environmental indices that can be correlated against village infection outcomes, including infection prevalence. A report of the work completed within the placement is expected.

Entry requirements

You should have a background in a quantitative subject (maths, physics, computer science, etc)

MSD 2: Biochemistry 
Cellular immunology to blood-stage malaria vaccines

Supervisor

Dr Carolyn Nielsen, Sir Henry Wellcome Postdoctoral fellow - Clinical Immunology, Nuffield Department of Medicine

Description

The research undertaken in the Draper group focuses on the development of novel and improved approaches to blood-stage malaria vaccine design, as well as aiming to better understand molecular mechanisms of vaccine-induced immunity to blood-stage malaria infection. You will contribute to this work by focusing on analysis of vaccine-specific cellular responses to blood-stage malaria vaccines from clinical trials run in Oxford. The project will include a literature review component to develop your understanding of the landscape of malaria vaccine development, followed by laboratory work focused on using a technique called flow cytometry to qualitatively and quantitatively assess vaccine-specific cellular responses, ie B cells and/ or T cells.

Project outcomes

You will develop skills and knowledge related to malaria and vaccine immunology, with a focus on cellular techniques such as flow cytometry. Data may help inform future immunology work in the group as part of the longer-term blood-stage malaria vaccine development programme.

Entry requirements

You should have a biology or medicine degree. Attention to details is required for safe and accurate lab work.

MSD 3: Cardiovascular Medicine 
Cardiac myosin function and heart disease

Supervisor

Dr Christopher Toepfer, Principal Investigator & Sir Henry Dale Fellow, Radcliffe Department of Medicine

Description

Myosin can be in two conformations in cardiac muscle a super relaxed state (SRX) or disordered relaxed state (DRX). DRX myosin drives muscle contraction and SRX myosin conserves energy in the heart. These states are affected by heart disease. But as of yet it is not known which heart diseases alter this key myosin state. The aim of the project is to learn to dissect cardiac tissues, measure the myosin states in these tissues by using fluorescent microscopy and uncover which diseases change myosin SRX and DRX.

Project outcomes

You will learn to perform tissue dissection, fluorescent microscopy and other general lab competencies including pipetting, keeping lab notes, and learning to perform statistical analysis. You will receive mentorship and establish a mentor network of researchers that can support your future research aims. You will be involved in a growing project that is well established experimentally with interesting outcomes to be understood about the role of myosin super relaxation in diseases outside of inherited cardiovascular conditions.

Entry requirements

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

MSD 4: Statistics in Medicine 
Quality of medical research

Supervisor

Dr Paula Dhiman, Senior Researcher in Medical Statistics, Nuffield Department of Orthopaedics / Rheumatology and Musculoskeletal Sciences

Description

At the UK EQUATOR Centre within the Centre for Statistics in Medicine, we are a team of statisticians and meta-researchers involved with many research studies looking at how medical research is done, how well it is reported in the published literature, and how it can be done better. We conduct many of our own methodological research studies, including systematic reviews and surveys. One example is that we are evaluating the use of artificial intelligence in cancer. We also work with other researchers and clinicians in designing and conducting their research studies. You will work on the ongoing research studies to gain experience and insight into all steps of doing and evaluating medical research studies; from working with the group to formulate research questions, design medical research studies, learn and improve how to code using specialised statistical analysis software, analyse data and help disseminate research through contributing to writing a research article. Which studies you will involved with will depend on your interests, but are most likely to include systematic reviews, methodological reviews, or prediction modelling.

Project outcomes

Your work may result in a co-authorship on publication and a presentation at departmental seminar

Entry requirements

There are no specific entry requirements.

MSD 5: Chemistry in Cells 
Developing fluorescent probes to image hypoxia in cells

Supervisor

Professor Stuart Conway, Professor of Organic Chemistry, Department of Chemistry

Description

Hypoxia, which means lower than normal oxygen, is found throughout biology, and is a common characteristic of tumours. Hypoxia in tumours causes problems with treatment of cancer, including making drug delivery difficult and making the tumours resistant to radiation. To help us identify and study hypoxia, we are developing probes that are not fluorescent under normal conditions, but which become fluorescent in hypoxia. The project will involve the chemical synthesis of some hypoxia-sensing probes, analysis of their fluorescent properties, and evaluation of the probes in cancer cells.

Project outcomes

You will be a part of the research team to develop probes that sense hypoxia in cells and a good training in interdisciplinary science.

Entry requirements

You will ideally be studying or have studied chemistry.

MSD 6: Chemistry and Pharmacology 
Synthesis of arylhydrocarbon receptor targeted degraders

Supervisor

Professor Angela Russell, Professor of Medicinal Chemistry, Department of Chemistry

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). You will design and synthesise 2-3 examples of AhR PROTACs building on our know how. If time permits they will also test their compounds for AhR antagonism, AhR degradation and utrophin upregulation in muscle cells.

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 synthesis of up to 3 AhR PROTACS ready for testing and 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). Your work is expected to form a substantial part of a publication describing the feasibility of AhR targeted degradation in utrophin modulation.

Entry requirements

You should be studying or have studied for a degree in chemistry or a degree where chemistry forms a substantial component of the degree.

MSD 7: Physiology, Anatomy & Genetics 
Epicardial epithelial-to-mesenchymal transition during heart development

Supervisor

Dr Joaquim Miguel Vieira, BHF Intermediate Basic Science Research Fellow, Department of Physiology / Anatomy & Genetics

Description

For this eight-week project in particular we aim to continue with the phenotyping of mouse models carrying a deletion of the cis-regulatory sequences located in intron 1 of the Wt1/Wt1as locus. Three mouse models were recently generated in the lab via CRISPR/Cas9 technology, and initial characterisation of mutant embryos at embryonic day (E) 15.5 by high-resolution episcopic microscopy (HREM) revealed thinner myocardial wall and impaired cardiac valve development for one of the mouse models. Therefore, future experiments will include the analysis of further samples by HREM so that all three mouse models are assessed in terms of global heart development. In addition, you will investigate the expression of WT1 and WT1as lncRNA in mutant versus control hearts by qPCR, immunofluorescence and multiplex in situ hybridisation (RNAscope), as well as implement ex vivo models (eg, epicardial explants derived from E11.5 hearts) to study epicardial EMT. Another experimental avenue to be explored includes investigating the role of the Wt1as lncRNA in EMT by using in-house mouse epicardial cell lines combined with growth factor stimulation of EMT in the presence or absence (eg, siRNA) of the noncoding transcripts. Alternatively, experiments to validate outcomes arising from CUT&RUN, ATAC-seq and scRNA-seq unbiased screens may be explored.

Project outcomes

You will contribute to ongoing research with potential contribution to scientific manuscripts, and development of technical skills, including mammalian tissue dissection, cell culture, primary tissue/explant assays, immunofluorescence, RNAscope, confocal/epifluorescence microscopy, HREM sample preparation and data analysis using AMIRA and HOROS software, and/or standard molecular biology techniques (eg RNA isolation, cDNA synthesis, qRT-PCR).

Entry requirements

Some understanding of cell and developmental biology, and gene regulation would be desirable.

MSD 8: Physiology, Anatomy & Genetics 
Lymphatic vessel formation

Supervisor

Dr Irina-Elena Lupu, Postdoctoral Research Scientist, Department of Physiology / Anatomy & Genetics

Description

This project will focus on the formation of the lymphatic vessels during embryonic development in the heart and it will form a part of a larger ongoing project in our lab. The techniques involved will include a mixture of molecular biology techniques such as PCR, cellular biology techniques such as confocal microscopy and data analysis techniques such as working with sequencing data. The project will also be tailored together with you to ensure you explore your areas of interest. The aim of the project is to understand the molecular control of lymphatic vessel formation in the heart.

Project outcomes

At the end of the project you will have a better understanding of how and when lymphatic vessel formation takes place in the mouse heart.

Entry requirements

There are no specific entry requirements.

MSD 9: Physiology, Anatomy & Genetics 
Regulation of physiological and pathological amyloidogenesis

Supervisor

Professor Clive Wilson, Professor of Cell and Development Genetics, Department of Physiology, Anatomy & Genetics

Description

Amyloidogenesis, the aggregation of specific proteins and peptides into fibrils, occurs normally, for example when secretory granules form in hormone-secreting cells, and pathologically, for example in Alzheimer’s Disease, where an abnormally cleaved product of the Amyloid Precursor Protein (APP) forms plaques. Using a new cell model to study granule formation in the fruit fly Drosophila melanogaster, we have shown that two human pathological proteins, one of them being APP, normally control the aggregation of these granules. This process is disrupted by pathological forms of these proteins. Since the granules we study are very large, we can visualise the entire amyloidogenic process at high resolution in real-time. In the project, the genetic mechanisms that control these different types of amyloidogenesis will be investigated. The project will involve microdissection, genetics, wide-field fluorescence imaging and bioinformatics.

Project outcomes

It should be possible to identify a novel regulator of amyloidogenesis and pinpoint the step in the process affected by this regulator.

Entry requirements

You should have experience in a relevant degree such as in biological sciences.

MSD 10: Experimental Psychology 
Understanding face processing in autism and prosopagnosia

Supervisor

Professor Geoff Bird, Professor of Cognitive Neuroscience, Department of Experimental Psychology

Description

This project aims to investigate face processing in prosopagnosic (‘face-blind’) individuals, and in individuals with autism. In particular, we will examine how individuals from these groups, and neurotypical individuals, remember someone’s name and other information about them (their job, how they know the individual, etc) when they see their face. You will be involved in design, programming, data collection and data analysis aspects of this project, providing breadth of experience in all aspects of research design and implementation.

Project outcomes

It is expected that this research study will result in a paper to be submitted for publication.

Entry requirements

Experience in psychology from your undergraduate degree is desirable but not essential.

MSD 11: Experimental Psychology 
Using computer games to investigate human concept learning

Supervisor

Lead: Professor Christopher Summerfield, Professor of Cognitive Neuroscience, Department of Experimental Psychology

Additional: Dr Tsvetomira Dumbalska, Department of Experimental Psychology

Description

We are interested in how people learn new concepts. A "concept" can be thought of describing a relationship between variables. For example, the concept of "aunt" describes the relationship between two individuals as a function of their gender and familial ties. We plan to measure concept learning by asking people to play simple video games. In these games, they will have to figure out what the concepts are, and use them to solve puzzles. We want to know the factors that help people learn new concepts fastest, and are interested in how the rate at which people learn new concepts correlates with traditional measure of academic ability. The project would involve collecting and analysing data from Oxford undergraduates.

Project outcomes

You will contribute to the ongoing project, carrying out data collection and analysis of experimental data with guidance from the supervisor. You will be credited with co-authorship on the resulting manuscript conditional on feasibility and an appropriate level of contribution.

Entry requirements

Some experience with coding is useful but not essential.

MSD 13: Clinical Neurosciences 
Functional analysis of genes causing cerebellar diseases

Supervisor

Associate Professor Esther Becker, Associate Professor of Neurobiology, Nuffield Department of Clinical Neurosciences

Description

The cerebellum is a fascinating brain structure that is involved in different functions. While the cerebellum has traditionally been regarded solely as a regulator of motor function, recent studies have demonstrated additional roles for the cerebellum in higher-order cognitive functions such as language, emotion, reward, social behaviour and working memory. Accordingly, cerebellar dysfunction is linked not only to motor diseases such as ataxia, dystonia and tremor, but also increasingly to cognitive affective disorders such as autism spectrum disorders and language disorders. We understand surprisingly little about the molecular processes that underlie the formation of the cerebellum and that, when disrupted, lead to disease. Research in our laboratory aims to elucidate these processes and provide novel insights into the genetic, molecular and cellular mechanisms that cause different diseases of the cerebellum. The project will focus on novel genetic mutations identified in our laboratory and study their functional consequences in cell lines and tissues using immunostaining and immunoblotting techniques.

Project outcomes

You will be familiar with the topic and with laboratory techniques including cell culture, immunostaining and immunoblotting. You will generate and interpret data that help us to understand the molecular basis of cerebellar diseases.

Entry requirements

You will ideally be studying or have studied biology, biochemistry or biomedical sciences.

MSD 14: Clinical Neurosciences 
The prevalence of autoantibodies in neuropathic pain

Supervisor

Associate Professor John Dawes, Associate Professor, Nuffield Department of Clinical Neurosciences

Description

This project will involve using samples from neuropathic pain patients as well as healthy controls and assessing IgG binding to cultures of both mouse primary sensory neurons and human stem cell derived sensory neurons as well as fixed nervous tissue samples. In addition, positive binders will then be assessed for potential pathogenic mechanisms including how they impact on neurite outgrowth, cell viability and neuronal physiology.

Project outcomes

You will join the research team to help in obtaining a better understanding of the prevalence of autoantibodies and may provide an improved insight for certain pain conditions. Moreover, the experiments on autoantibodies can be utilized as a tool to provide a broader comprehension into clinically relevant mechanisms controlling pain sensitivity and enhance the development of therapies to treat neuropathic pain. On a practical aspect, you will gain further experiences across multiple techniques and laboratory studies, such as primary cell culture, immunohistochemistry, microscopy and data analysis.

Entry requirements

Previous lab experience is desirable but not essential.

MSD 15: Nuffield Department of Medicine 
Variants of unknown significance

Supervisor

Dr Alexandra Martin-Geary, Postdoctoral Bioinformatician, Nuffield Department of Medicine

Description

Since the first human genome sequence was completed in 2003, our ability to capture and map genetic variation on both an individual and population level has expanded swiftly, giving rise to large repositories of human genetic data. Whilst the rapid progress made over the last 18 years has proven invaluable for our understanding of genetic disease, we are now faced with the novel issue of having amassed an overabundance of potentially clinically relevant variants, but for which the disease mechanism/contribution to disease is as yet unknown. Using computational methods, we aim to address this dearth of information, with a focus on variants of unknown significance in the untranslated regions of human genes.

Project outcomes

You have have an introduction to some of the main methods used to identify and interpret potentially disease causing genetic variation. You will learn some programming and how to use a selection of the major tools and datasets used in computational rare disease analysis. You will also get to experience what life is like in a lab environment where the ethos is built around kindness and a shared drive to shed light on the underlying causes of rare human disease

Entry requirements

A biology/genetics focused background is desirable

MSD 16: Nuffield Department of Medicine 
Molecular pathogenesis of Parkinson’s disease

Supervisor

Associate Professor Ira Milosevic, Nuffield Department of Medicine

Description

Parkinson’s disease (PD) imposes a significant burden on patients, families and society, and will become of even greater concern as life expectancy increases, and the world population continues to age. Thus, the molecular underpinnings of this disorder need to be understood in a timely manner. Defective intracellular trafficking has been linked to ataxias and PD. However, the connection between impaired intracellular trafficking and the pathological characteristics of these diseases is not well understood. Within intracellular trafficking, several points of interest have been highlighted by various studies. These include exocytosis and endocytosis, and endo-lysosomal pathway. Several genes linked either hereditarily or as risk factors in these processes have been identified. You will join ongoing work on understanding the function of some key PD-risk candidates in neuronal cells. You will be trained in basic proteomics (Western blotting), cell culture and, if time permits, imaging.

Project outcomes

You will join an ongoing project that studies molecular pathogenesis of Parkinson’s disease, and will be encouraged to read and discuss the relevant literature, as well as perform several experiments under supervision. Training will be provided.

Entry requirements

Previous lab experience and/or experience in culturing cells are desirable but not essential.

MSD 17: Nuffield Department of Population Health 
Cancer epidemiology

Supervisor

Dr Christiana Kartsonaki, Senior Statistician, Nuffield Department of Population Health

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 project will lead 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.

MSD 18: Nuffield Department of Primary Health Care Sciences 
Morbidity among long-term survivors of non-malignant meningioma

Supervisor

Dr Diana Withrow, Medical Statistician, Nuffield Department of Primary Care Health Sciences

Description

Meningioma is the most frequently diagnosed brain tumour in the UK. At least 80% of meningiomas are non-malignant, and a subset of these are diagnosed asymptomatically upon imaging for another indication. First-line management for these tumours is most commonly observation or surgery. The survival from non-malignant meningiomas is relatively high, but the tumours and their treatment often lead to long-term complex health needs among survivors, resulting in an increased healthcare burden. The specific health challenges facing meningioma survivors in the years following their diagnoses, however, are poorly understood. In the proposed study you will explore morbidity among long-term survivors of non-malignant meningioma using large, administrative linked datasets.

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.

MSD 19: Nuffield Department of Women’s & Reproductive Health 
Characterisation of hormones regulating lactation

Supervisor

Professor Fadil Hannan, Director of the Larsson-Rosenquist Foundation Oxford Centre for the Endocrinology of Human Lactation, Nuffield Department of Women’s & Reproductive Health

Description

Lactation is critical for promoting optimal infant development, and also protects the mother from diseases such as breast and ovarian cancer and type 2 diabetes. A range of reproductive and metabolic hormones are involved in milk synthesis and secretion, however the underlying cellular processes are incompletely understood. The aim of this laboratory-based project is to investigate the effect of hormones on gene expression and metabolism in mammary cells. Methodologies utilised in this project include maintaining cultured mammary cells, assessment of gene expression using quantitative reverse transcriptase PCR (qRT-PCR), and continuous monitoring of cellular metabolism by measuring media oxygen and pH.

Project outcomes

You will gain experience with undertaking research into the endocrinology of lactation, and new insights into the cellular basis of hormone action.

Entry requirements

There are no specific entry requirements.

MSD 20: Oncology 
Immune populations of the tumour microenvironment

Supervisor

Dr Monica Olcina, Group Leader - Immune Radiation Biology, Department of Oncology

Description

The Olcina lab is focused on understanding immune biology to improve radiotherapy. We are particularly interested in how the tumour microenvironment contributes to tumour-specific dysregulation of innate immunity pathways such as the complement system. You will explore the spatial relationship between key innate and adaptive immune components of the tumour microenvironment (TME). The project will involve analysis of multiplex imaging data to assess such spatial distribution.

Project outcomes

You will establish an analysis pipeline for assessing the spatial distribution of immune populations within the tumour microenvironment.

Entry requirements

There are no specific entry requirements.

MSD 21: Oncology 
Identification of novel targets to radio-sensitise hypoxic cells

Supervisor

Dr Hannah Bolland, Postdoctoral Researcher, Department of Oncology

Description

The project aims to validate the results from an siRNA screen. This screen identified novel targets that if we inhibit, make cancer cells more sensitive to radiation. This project will aim to investigate the mechanism of how inhibition of these genes radio-sensitise cells. The main skill that you will develop on this project is western blotting

Project outcomes

The outcome will be to generate one useable figure for a publication. Samples will be provided for you to run western blots with to try and identify the mechanism of cell death or radio sensitisation.

Entry requirements

You should ideally have a background or experiences in biology or chemistry.

MSD 22: Botnar Research Centre 
Pharmaceutical interventions for ageing (autophagy)

Supervisor

Dr Ghada Alsaleh, Versue Arthritis Career Development Fellow, The Kennedy Institute Of Rheumatology; Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences

Description

Ageing research has made significant progress over recent years, giving us several candidate hallmarks that are generally considered to contribute to the ageing process. Autophagy is implicated in a majority of these ageing hallmarks. The fact that autophagy declines with age and that inducing autophagy reverses ageing makes autophagy a promising therapeutic target. We recently found that the TFEB pathway, a pathway controlling autophagy, is often dysregulated in age and crucial for preventing cellular ageing and maintaining normal cell function. We aim to identify a drug targeting TFEB, so we performed a selective drug screen to identify compounds that increase TFEB expression in OA models. In this project, you will validate the effects of identified drugs on improving cartilage degradation and elucidate the role of TFEB in human tissue obtained from donors with OA and in an OA model in young and old mice.

Project outcomes

You will be a part of the team to provide data and rationale for funding an experimental medicine trial for the treatment of OA. Furthermore, Your work will yield outputs to support follow on efforts to develop pharmaceutical interventions for aging leveraging autophagy via the TFEB pathway.

Entry requirements

There are no specific entry requirements.

MSD 23: Radcliffe Department of Medicine 
Novel peptides and inflammatory disease

Supervisor

Lead: Professor Shoumo Bhattacharya, Professor of Cardiovascular Medicine, Radcliffe Department of Medicine

Additional: Graham Davies, Radcliffe Department of Medicine

Description

Our groups’ interests are in anti-inflammatory proteins from nature, specifically from ticks and viruses. We have identified specific regions of these proteins which maintain this anti-inflammatory effect. You will work on these novel peptides to determine their biochemical (eg binding to target by fluorescent polarisation, protein-interaction disruption using bead/plate-capture assays) and biological activity (eg cell migration assays) and elucidate their potential effect on inflammatory diseases. Project areas: protein interactions, inflammation, chemokine, phage-display, next-generation sequencing, theragnostics

Project outcomes

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

Entry requirements

A biological/scientific background is required.

MSD 24: Pathology 
Quantifying organelle growth

Supervisor

Professor Jordan Raff, Sir William Dunn School of Pathology

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.

Project outcomes

You will learn some fly genetics and embryo manipulation and how to use sophisticated microscopy systems and the computational methods needed to extract quantitative data. No prior experience in any of these techniques or methods is required.

Entry requirements

There are no specific entry requirements. An interest in biology and/or computational image analysis is desirable.

MSD 25: Pathology 
Genetic investigation of morphogenesis of the neuromuscular junction

Supervisor

Dr Luis Alberto Baena Lopez, Associate Professor, Sir William Dunn School of Pathology

Description

Locomotion requires fine-tuning between the nervous system and the muscles. Consequently, A desynchronized neuro-muscular activity often lies at the heart of neurodegenerative diseases. Transferable to human development, we can use the fly larvae as a model system to investigate the morphogenesis of the neuromuscular junction (NMJ). Our preliminary results indicate that cell death-related proteins may execute death unrelated functions relevant for correct NMJ formation but further investigations are needed. To this end, we will induce genetic loss-of- or gain-of-function of those proteins of interest to then evaluate their physiological impact during NMJ morphogenesis. As part of the project, we will also generate two new plasmids helping us to facilitate the overexpression of proteins of interest. The project will require a wide range of techniques such as Drosophila genetics, microdissection, immunohistochemistry, fluorescence microscopy, molecular cloning, and statistical analysis, which together aim to provide valuable training to you.

Project outcomes

In this project, you will gain novel insights into the molecular pathways that ensure correct NMJ morphogenesis, a process that can turn awry in human pathologies such as muscular dystrophies. You will receive training in a diverse and widely used range of laboratory techniques and learn practical skills.

Entry requirements

There are no specific entry requirements.

MSD 26: Wellcome Trust Centre for Human Genetics 
Monitoring highly pathogenic emerging viruses in West Africa

Supervisor

Professor Miles Carroll,Carroll Group, Wellcome Centre for Human Genetics, Nuffield Department of Medicine

Description

West Africa is the site of continuous spill over events of viruses from wild animals to humans. It is difficult to catch the virus in the act when sampling humans so we look for footprints left behind by the pathogens, in the form of antibodies, as proof of virus infection. We apply highly specific assays such as ELISA to identify antibody responses. Mapping these spill over events helps us to identify the high risk areas that will likely give rise to future outbreaks and epidemics. You will be trained in antibody detection techniques and subsequent statistical analysis of results.

Project outcomes

Evidence of pathogenic virus spill over events in specific regions within West Africa. Data will be fed back to local outbreak response units, primarily within the Republic of Guinea. Ultimate aim is to publish the data so the international community has access to the sero-epi data set.

Entry requirements

Experience from a relevant undergraduate degree in biological sciences would be helpful.

MSD 27: Population Health 
Phenotyping measures of fat from cardiac images

Supervisor

Professor Jemma Hopewell, Professor of Precision Medicine & Epidemiology, Nuffield Department of Population Health

Description

Large-scale epidemiological studies have established the importance of body fat distribution for atrial fibrillation. However, the role of epicardial fat (fat around the heart) remains unclear, partly due to complexities in phenotyping at scale. We have been leading phenotype generation from cardiac magnetic resonance (cMR) images in order to elucidate this relationship using subsequent epidemiological and genetic epidemiological analyses. You will play a key role in validating new epicardial fat measures from cMR images, which is pivotal in demonstrating reproducibility. You will contribute to understanding factors that affect phenotype quality and examining relationships with patient characteristics. You will be part of the Hopewell Group, a multidisciplinary team based at the Big Data Institute. You will receive career mentorship and hands-on training in cMR image-related skills, essential statistical and programming techniques, and will gain an appreciation of the value of big data (such as UK Biobank) in epidemiological and genetic epidemiological studies.

Project outcomes

Based on appropriate training, supervision and support from the primary investigator (PI) and a clinical research training fellow (as well as other team members and collaborators) you will develop an understanding of basic cardiac anatomy and skills in cardiac image analysis and undertake related phenotyping. You will also analyse resulting data to assess reproducibility of measures of epicardial fat area. You will develop a fundamental understanding of relevant medical statistic techniques and basic statistical programming skills. You will understand the value of big data for cardiovascular research, and key epidemiological and genetic epidemiological principles and techniques. You will actively participate and present in team meetings, and provide end-of-project deliverables.

Entry requirements

A background in medical/biological sciences, with previous quantitative experience/basic statistical training would be highly advantageous. Training on required techniques will be provided during the placement.

MSD 28: Oxford Cancer 
MHC class I allotypes

Supervisor

Professor Tim Elliott, Kidani Professor of Immuno-Oncology, Nuffield Department of Medicine

Description

"Major Histocompatibility Complex class I (MHC I) molecules are found on the surface of all nucleated cells in the bodies of vertebrates. Their role is to present peptides derived from all the cells proteins in order to allow Cytotoxic T cells to discriminate between peptides that are presented by healthy cells, and those peptides presented by infected or cancerous cells – with such cells being targeted for destruction. The loading of peptides onto is facilitated by a protein called tapasin. MHC I molecules are highly polymorphic, with different allotypes being reliant on tapasin to different degrees [1-3]. While the variation in dependence upon tapasin for efficient peptide selection has been known for several decades, the molecular cause of this variation has still to be determined.

Project outcomes

You will identify potential molecular sequence characteristic(s) that underpin dependence on tapasin for efficient cell surface expression. You will also compare two provided molecular dynamics trajectories of MHC I allotypes (B*44:02 and B*44:05) using the MDAnalysis python package. You will develop your knowledge of informatics.

Entry requirements

You should have some informatics understanding. This project is especially suitable for “non bio” students (eg engineers, physicists, etc).

MSD 29: Oxford Cancer 
Myeloid cell effector function

Supervisor

Lead: Professor Irina Udalova, Professor of Molecular Immunology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences

Additional: Dr Laura Myhill, University of Copenhagen

Description

Diet-derived fibres and lipids are an important energy source for host cells with immunomodulatory properties. Dietary fibre fermentation in the colon by commensal microbiota results in the production of metabolites, such as short chain fatty acids (SCFAs), which are well-known to suppress colonic mucosal inflammation. Similarly, dietary polyunsaturated fatty acids (PUFAs), have also been shown to exert immunomodulatory effects. Products of ω-3 PUFA generally demonstrate anti-inflammatory effects potentially influencing cancer risk; while products of w-6 PUFAs are considered pro-inflammatory. These diet-host immune interactions result in alterations to tissue microenvironments, and as such may exert beneficial changes but also contribute to development of disease, inflammation, or, as we have recently demonstrated, susceptibility to infection. In fact, westernised diets composed of saturated fats and refined dietary fibre have been associated with the increased propensity of developing immunometabolic and inflammatory diseases, as well as cancer.

Project outcomes

You will develop lab skills in immunology/biochemistry, and aim to achieve the research outcomes of the objectives of the project.

Entry requirements

You should have a strong interest in and ideally some lab experience in immunology and/or biochemistry.

MSD 30: Oxford Cancer 
Molecular architecture of bowel and womb cancer

Supervisor

Dr David Church,  Nuffield Department of Medicine

Description

Cancers of the bowel and womb are common, and cause considerable suffering and death. We study the biology of these cancers to better understand and treat them.

Project outcomes

Your research findings could be included in a future research study and you will also gain valuable experience of laboratory work.

Entry requirements

There are no specific entry requirements.

MSD 12: Information Engineering is listed under Medical Sciences on the application form but it is a Mathematical, Physical and Life Sciences Project.

Social sciences projects

SSD 1: Archaeology 
Reconstructing detailed volcanic eruption records

Supervisor

Professor Victoria C. Smith, Professor of Tephrochronology, School of Archaeology

Description

Accurate volcanic eruption records are essential for hazard assessments. We are aiming to assemble longer and more detailed eruption records for volcanoes around the incredibly populated Naples (Italy) and Mexico City. This requires the integration of stratigraphic sequences that contain volcanic eruption deposits (tephra), which we can correlate using the composition of the volcanic glass or minerals that make up these tephra units. You will involve instruction in the basics of tephrochronology, preparation of the samples in the laboratory, geochemical analysis of the samples using an electron microprobe, and training on use the geochemical data for correlation. Amalgamating information from the records will provide a better understanding of the explosive volcanism in the regions and how this has varied over time.

Project outcomes

You will process geochemical data, correlations and reconstruction of the records that will help with ongoing volcanic research in the regions and will probably be included in publications.

Entry requirements

You should ideally have a background in earth science, geography or archaeology.

SSD 2: Archaeology 
Endangered archaeology sites in the Middle East region

Supervisor

Dr Michael Fradley, Postdoctoral Researcher, School of Archaeology

Description

The EAMENA project uses open-source satellite imagery to identify, document and monitor archaeological sites across the Middle East and North Africa to help support research and future management of heritage in the region. You will be trained and supported in using these earth observation skills to survey a defined area of the Middle East, with the principal aim of providing a baseline documentation of the region and uploading this data to the EAMENA open-access database. You will focus on the optical analysis of satellite imagery, identifying archaeological features within the project study area, and recording the impact of modern human activity on these sites.

Project outcomes

You will contribute to the wider aims of the EAMENA project by creating data for the EAMENA database which will be available for use by researchers and heritage managers. You will also have the option to produce direct outputs such as reports or blogs to promote their work on the project. At a broader level you will be provided with transferable earth observation skills in the use of satellite imaging and managing geospatial data that can be utilised in a wide range of disciplines and professions.

Entry requirements

There are no specific entry requirements.

SSD 3: Archaeology 
Tracing elusive female scholars in Chinese archaeology

Supervisor

Dr Anke Hein, Peter Moores Associate Professor in Chinese Archaeology, School of Archaeology

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

Chinese-language skills are not required though they would be useful. Familiarity with excel or databases would be useful but is not required and training is available. Some familiarity with principles in anthropology and/or history is useful but likewise not required. The main thing needed is curiosity and basic competency with computers and internet searches.

SSD 4: Education 
Children’s learning with mobile applications

Supervisor

Dr Sophie Booton, Research Officer, Department of Education

Description

This project aims to assess the educational impact of app-based interactive activities for young children. The internship will involve two main tasks, which can be flexible to your skills and interests: 1) preparation and analysis of data from an evaluation of a vocabulary app for children, including learning analytic data from users of the application, and 2) preparation of materials for a study of the impact of creativity apps on children’s creative behaviour. Potential skills developed will include data management, statistical analysis, research design, data collection with children, ethics, measure development, behavioural coding, and report writing.

This internship may be funded by 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 £2,600 before tax and employee National Insurance). Please refer to the About and Eligibility criteria sections of this page for further details about ESRC-funded placements.

Project outcomes

You will conduct an evaluation of a vocabulary app for children, and/or produce materials for a study of the impact of creativity apps on children’s creative behaviour, including an adapted measure for assessing children’s creative behaviour.

Entry requirements

You should have experience from a relevant degree in psychology, linguistics, or education. Some training in statistical analysis and software (eg SPSS or R)

SSD 5: Nuffield College/Social Investigation 
Stop and search practices in London

Supervisor

Dr Thiago R. Oliveira, Research Fellow, Nuffield College

Description

Several police organisations worldwide rely upon stopping and questioning members of the public who are deemed suspicious of past or future criminal activity to tackle serious crime. In the United Kingdom, Section 60 allows police officers to stop and search members of the public even without prior suspicion. Yet, the effect on crime of this type of policing strategy is still unclear, and the potential existence of several collateral consequences means that this remains a controversial topic in criminology and social policy. You will analyse police and crime data from London, and will assess the spatial association between stop and search, crime rates, and other variables. Results from this project will be featured in an academic paper and/or policy report discussing the effectiveness of Section 60 stop and search practices in London. Interns will gain skills in quantitative research methods, data management, data analysis, and discussion of policy-relevant results.

Project outcomes

Your work will be featured as part of this project in an academic paper and/or policy report discussing the effectiveness of Section 60 stop and search practices in London.

Entry requirements

There are no specific entry requirements.

SSD 6: Nuffield College/Social Investigation 
Disorder and prosocial and antisocial behaviour

Supervisor

Dr Charles Lanfear, Research Fellow, Nuffield College

Description

The effect of disorder on norm-violating behaviour is the subject of an ongoing controversy in criminology and social psychology. A series of experiments in the Netherlands found disorder induced antisocial behaviour, while a large-scale replication in the United States found no effects. This project will replicate a past field experiment on the effects of physical disorder on prosocial and antisocial behaviour in the UK context. You will manipulate litter and graffiti near post-boxes at multiple sites and record differences in theft (antisocial behaviour) or mailing (prosocial behaviour) of an envelope containing money. Results from this project will be compared to past results in the United States and the Netherlands, and featured in an article summarizing related field experiments. Interns will gain skills in field experiment methods, data recording, data analysis, and presentation of experimental results.

Project outcomes

You will produce a report summarising the theoretical background, methods, and results of the field experiment.

Entry requirements

You should have some background in sociology, social psychology, psychology, or criminology.

SSD 7: Geography and the Environment 
UNESCO heritage sites

Supervisor

Lead: Professor Heather Viles, Professor of Biogeomorphology and Heritage Conservation, Associate Head (Research) Social Sciences Division, School of Geography and the Environment

Additional: Dr Martin Michette, School of Geography and the Environment

Description

This project will support research in understanding the links between natural and cultural heritage. The aim is to better understand UNESCO listing in relation to mixed heritage and cultural landscape sites which have many overlaps. Mixed heritage listing requires a site to meet both cultural and natural significance criteria, while UNESCO define cultural landscapes as “Combined works of nature and humankind, they express a long and intimate relationship between peoples and their natural environment”. Starting with a review of the characteristics of sites listed as mixed or cultural landscapes, the project will be mainly desk based. You will review UNESCO documents and other relevant literature and will be encouraged to reach out to relevant experts as they develop more specific questions. If possible, field visits to UNESCO sites in the UK will help develop case.

Project outcomes

The project will fill a knowledge gap within OxRBL related to understanding the meaning and relevance of UNESCO listing for several of our heritage science projects, including SXNCH (Sites at the Intersection of Natural and Cultural Heritage), NICHE (Nature Interacting with Cultural Heritage Environments), and our work at UNESCO listed sites including Petra, Lalibela, and the Tower of London. You will produce a report (perhaps in the form of an Arc GIS story map) designed for an audience of graduate students, researchers and heritage practitioners.

Entry requirements

We look for applicants with motivation and willingness to learn new skills rather than having any specific study experience to date.

SSD 8: Sociology 
Violence as a social determinant of health

Supervisor

Dr José Manuel Aburto, Newton International Fellow, Department of Sociology

Description

You will analyse different typologies of violence and quantify their impact on population health (eg homicide mortality, fear of crime, life expectancy) through demographic techniques.

This internship may be funded by 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 £2,600 before tax and employee National Insurance). Please refer to the About and Eligibility criteria sections of this page for further details about ESRC-funded placements.

Project outcomes

You will produce a report or article based on your research findings.

Entry requirements

There are no specific entry requirements.

SSD 9: Nuffield College/ Sociology 
Social and environmental determinants of sleep patterns

Supervisor

Professor Melinda Mills, Nuffield Professor of Sociology, Director Leverhulme Centre for Demographic Science, Department of Sociology

Description

The project will consist of two parts (you will be able to choose what component you are more interested in). Substantial topic of the project is to inquire into social and environmental determinants of sleeping patterns. The first component is a literature review - the idea is to introduce methods concerning the analysis of literature. We will cover such topics as literature search, filtering, analysis and will give brief workshops on scoping reviews and pre-registration along with systematic reviews and meta-analyses. You will learn and be able to use these skills in your future academic work. The second component will consist of developing basic data analysis skills including data gathering, cleaning, and visualisations.

This internship may be funded by 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 £2,600 before tax and employee National Insurance). Please refer to the About and Eligibility criteria sections of this page for further details about ESRC-funded placements.

Project outcomes

You will develop literature review and basic data analysis skills via conducting an empirical research. At the end of the internship you will be expected to either produce a written report or give a presentation to the group.

Entry requirements

There are no specific entry requirements.

SSD 10: FAME, Saïd Business School 
Managing discrimination

Supervisor

Professor Renée B Adams, Professor of Finance, Saïd Business School

Description

Workplace discrimination violates human rights and is costly to organisations. This raises important questions about why it persists. You will examine how organizational leaders respond to and manage discrimination using detailed board meeting data from NHS hospital trusts. The project focuses on responses of NHS trust boards to two types of discrimination: discrimination internal to the trust, which is revealed in NHS employee surveys, and discrimination in society, which we measure using the Black Lives Matter Movement. To examine how boards respond to discrimination, you will conduct textual analyses of board meeting minutes and link the text to data from NHS employee surveys and data on individual board members and the hospital trust. You will analyse the data using standard econometric regression techniques. The goal will be to uncover factors that lead boards to develop effective responses to discrimination. For example, you will examine whether "lived experience" is important for addressing discrimination, ie whether directors from minority backgrounds are better able to address discrimination and whether government regulations help reduce discrimination.

Project outcomes

You will help to produce a set of papers on how organisational leaders manage discrimination that are aimed at publication in top academic journals, as well as a project website that provides write-ups of results for organizational leaders and other members of the general public who are interested in discrimination.

Entry requirements

Coding and programming experience would be useful, as well as knowledge of and experience with statistics and econometrics. We welcome applicants with an interest in the topic.

SSD 11: International Development 
Refugee Economies Programme

Supervisor

Professor Alexander Betts, Professor of Forced Migration and International Affairs, Department of International Development

Description

The Refugee Economies Programme does research on the economic lives of refugees and their impact on host communities, with a focus on East Africa. The programme undertakes inter-disciplinary mixed methods research and covers economics, political science, and anthropology. The work looks at refugees in cities and camps in Kenya, Ethiopia, and Uganda, including through a longitudinal study of 16,000 refugees and proximate host community members. We explore themes such as refugee entrepreneurship, social cohesion, migration and mobility, the determinants of socio-economic outcomes (including physical and mental health), and the impact of market-based interventions (such as cash-based assistance). The Programme uses participatory research methods and runs a small research hub in Nairobi which trains and mentors refugee researchers. The research is mainly supported by the IKEA Foundation, and engages with business, governments, and NGOs.

This internship may be funded by 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 £2,600 before tax and employee National Insurance). Please refer to the About and Eligibility criteria sections of this page for further details about ESRC-funded placements.

Project outcomes

You will work on the Refugee Economies Dataset, especially on regression analysis or descriptive statistics, mainly using STATA. You will also undertake some qualitative research to assist with ongoing work.

Entry requirements

You should have experience in a relevant social science discipline (eg economics, political science, sociology, anthropology) from your undergraduate degree and an interest in refugees and international development. Some background in either quantitative research methods (use of STATA) would be highly desirable but not essential.

SSD 12: Politics and International Relations 
Defence arguments in international criminal trials

Supervisor

Dr Yuna Han, Departmental Lecturer in International Relations, Department of Politics and International Relations

Description

What are the effects of defence arguments on the broader legal practice of international criminal justice? International criminal trials require robust defence for their legitimacy. And yet, both in practice and scholarly discourse, specific attention to how defence practices shape international criminal justice more broadly is lacking. The broader project addresses this gap by focusing on defence arguments, or discourse presented by defence counsels on behalf of defendants in international criminal trials, as a form of norm contestation. In advancing defendants’ interests, defence arguments can present different values, interpretation of international norms, and advance alternative narratives about the causes and responsibilities regarding the alleged crimes. Your work will contribute to the study of defence arguments by assisting in building a qualitative dataset of international criminal defence arguments by coding court transcripts and, where possible, assisting with interviews with defence team members.

This internship may be funded by 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 £2,600 before tax and employee National Insurance). Please refer to the About and Eligibility criteria sections of this page for further details about ESRC-funded placements.

Project outcomes

You will produce an article with a qualitative dataset of international criminal defence arguments, coded by using qualitative data analysis software.

Entry requirements

Knowledge/ prior education in international relations (particularly constructivist IR theory) and some knowledge of international criminal law would be helpful. Understanding of qualitative research methodologies (particularly thematic discourse analysis) would also be useful, but not essential.

SSD 13: Politics and International Relations 
Nationalizing a pandemic

Supervisor

Dr Marnie Howlett, Departmental Lecturer in Politics (Qualitative Methods), Department of Politics and International Relations

Description

Without a coordinated international approach to the COVID-19 pandemic, state governments have individually reacted to the spread of the virus since March 2020 by enforcing their own social distancing, lockdown, and border control measures. In an attempt to generate a collective domestic response, and secure the national ‘we,’ political leaders have turned their attention inwards using nationalistic rhetoric to appeal to their citizens’ feelings and emotions, and, in many cases, to build support for strict social distancing and lockdown measures. This reality has undoubtedly lead to increasingly new and inconsistent socio-economic challenges across states, especially within the communities located closest to state borders who previously regularly engaged in cross-border interactions. This project therefore explores the local impacts of domestic government responses to the COVID-19 pandemic in the lives of the people most directly impacted by these actions. The study is particularly interested in investigating the implications of states’ positions and policies regarding mobility, vaccines, and social distancing for citizens’ feelings of attachment to their state, and the ways that nationalism is understood and expressed in these areas.

This internship may be funded by 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 £2,600 before tax and employee National Insurance). Please refer to the About and Eligibility criteria sections of this page for further details about ESRC-funded placements.

Project outcomes

The analysis of this pilot project is centred on Ukraine. The project utilises a mixed-method design that analyses interviews and political speeches along with survey and social media data to understand grassroots narratives and discourses regarding government policies and the COVID-19 pandemic. You will be involved in a mix of transcribing interviews, regression analysis and/or descriptive statistics, and coding speeches, policy memos, and social media data. There is also scope to undertake some qualitative data collection and analysis (particularly content and discourse analysis) to assist with ongoing work.

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.

SSD 14: Politics and International Relations 
Disappearing seas

Supervisor

Dr Hussam Hussein, Departmental Lecturer and Research Fellow in International Relations, Department of Politics and International Relations

Description

This research project focuses on shading light on what seas, lakes, and freshwater courses have disappeared in the past century, exploring the reasons and causes behind this. You will map lakes and freshwater courses that have drastically reduced or that have disappeared in the past century, such as the Owens Lake, the Aral Sea, and the Dead Sea, trying to unpack the drivers of such environmental changes. Amongst the tasks, you would also suggest initial analysis on the different cases and the underlying reasons and causes.

This internship may be funded by 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 £2,600 before tax and employee National Insurance). Please refer to the About and Eligibility criteria sections of this page for further details about ESRC-funded placements.

Project outcomes

You will produce a literature review, a short report on the cases selected, including maps, and a table with potential explanatory factors.

Entry requirements

You should have an interest in environmental governance and political geography.

SSD 15: Politics and International Relations 
Location of military bases: Brexit and voting patterns

Supervisor

Dr Daniel Devine, Career Development Fellow in Politics, Department of Politics and International Relations

Description

Does the existence of military bases change voting patterns and support for Brexit? This project aims to understand this question. It is plausible that, due to a variety of factors, military bases might increase support for right-wing politics; this might be because of where the base is placed in the first instance, a diffusion effect amongst civilians in the area, or the number of military personnel in the surrounding areas. You will join the research team, working with colleagues at the University of Southampton, will collect geo-locations of all military bases in the UK, combine it with data on voting patterns in specific areas, and run regression and spatial regression models to determine whether the location of military bases matter for these outcomes.

This internship may be funded by 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 £2,600 before tax and employee National Insurance). Please refer to the About and Eligibility criteria sections of this page for further details about ESRC-funded placements.

Project outcomes

You will produce a number of analyses, potentially leading to an academic paper but certainly public outputs. You will gain experience working on an academic project, data analysis and presentation, and work with different colleagues.

Entry requirements

Preferably, experience in social sciences (eg politics, economics) from your undergraduate degree, and an interest in quantitative work (ie using numerical data). Knowledge of the programme R is desirable but not essential.

SSD 16: Nuffield College/Economics 
Social network based climate change concerns

Supervisor

Lead: Professor Martin Ellison, Professor of Economics, Department of Economics

Additional: Dr Susana Martins, Nuffield College

Description

Existing climate change news or media awareness indices based on newspapers may not provide the best measure of people's concerns about climate change. When people talk (tweet) about climate change news, we are guaranteed they have read it and are aware of it. The idea is to bridge the gap between social media and social network awareness and concerns about climate change. The Twitter API will give us access to the text content of tweets. To build the new climate change social network awareness index, a score can be assigned to each tweet based on the relevance of its climate change content measured by applying text mining. The index will thus measure the fraction of the text content of tweets dedicated to the topic of climate change on a given day. This allows direct comparison to the score given to the newspaper on the same day.

This internship may be funded by 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 £2,600 before tax and employee National Insurance). Please refer to the About and Eligibility criteria sections of this page for further details about ESRC-funded placements.

Project outcomes

A social network based climate change awareness index subdivided by climate change topics, themes and sentiment. Aggregate results may be made publicly available to help assess and monitor unexpected increases in climate change awareness and concerns.

Entry requirements

There are no specific entry requirements.

SSD 17: Nuffield College/Immigrant Youth 
International data and the integration of immigrant youth

Supervisor

Dr Olivia Spiegler, Research Fellow and Postdoctoral Researcher, Nuffield College

Description

You will get access to the Children of Immigrants Longitudinal Survey in Four European Countries (CILS4EU) to study aspects of sociocultural and psychological integration among immigrant-origin youth. This project promotes a solid understanding of the psychological aspects of integration. The project also promotes analytical skills and familiarity with the most commonly used software packages to analyse large-scale data sets.

Project outcomes

You will conduct a thorough literature research to familiarise yourself with the most relevant empirical and theoretical research articles in the field. Equipped with a solid overview of prior research, you will then formulate a research question and hypothesis (eg, second-generation immigrant youth are better integrated than first-generation immigrant youth). You will use the software packages SPSS and/or R to run simple analyses to test your hypothesis. Finally, you will write a brief research report based on your findings.

Entry requirements

A strong affinity to statistical analyses and interest in migration and integration are required.

SSD 18: Nuffield College/Social Policy and Intervention 
Helping empower youth brought up in adversity

Supervisor

Professor Lucie Cluver, Professor of Child and Family Social Work, Department of Social Policy and Intervention

Description

The HEY BABY study focuses on understanding the circumstances of young parents (<18 years) living in low socioeconomic communities in South Africa where there is a high prevalence of HIV. We have collected detailed information from 1000 adolescent parents about their lives and also the health and wellbeing of their children. You will use statistical methods to analyse this data and answer two primary questions: “What puts adolescent parents and their children at risk of disadvantage?” “What can help adolescent parents and their children achieve their full potential”? The team conduct this research with a number of international organisations that are responsible for making high-level decisions about how best to support young parents, for example the South African Government, The World Health Organisation (WHO), The United Nations International Children’s Emergency Fund (UNICEF).

This internship may be funded by 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 £2,600 before tax and employee National Insurance). Please refer to the About and Eligibility criteria sections of this page for further details about ESRC-funded placements.

Project outcomes

At the end of the project, you will have a primary understanding of the principles, methods, and practices of social science research, how research can inform high-level political decisions, and what it is like to work in a pioneering research team. You will be expected to read and summarise literature about the experience of adolescent parents, work with and learn from postdoctoral researchers in the Young Carers, SA team, and develop policy briefs summarising research findings to be shared with policy makers.

Entry requirements

You should have a background in mathematics.

Eligibility criteria for entry in 2022

You do not have to be studying at the University of Oxford to be eligible. You may apply to both UNIQ+ and the Wellcome Biomedical Vacation Scholarships at Oxford if you wish and you only need to submit one application. However, your application will only be considered for programmes where you meet the eligibility criteria.

To be eligible for UNIQ+ you must:

  1. be ordinarily resident in the UK (your UK residence should not have been wholly or mainly for the purpose of receiving full-time education, eg you moved to the UK for educational purposes at the start of your course); and
  2. be currently undertaking or have completed an undergraduate degree at a UK or Irish university; and
  3. be in at least the second year of your course if you have not graduated yet; and
  4. not have already completed or be currently studying a PhD/DPhil, or have an offer to start a PhD/DPhil (see below for advice if you have applied for PhD/DPhil study and are waiting to hear the outcome); and
  5. meet at least one of the following criteria:
    • be in the first generation of your family to go to university (eg neither of your parents have an undergraduate degree); or
    • be care experienced (for a period of more than 3 months); or
    • have had caring responsibilities for 3 months or more occupying more than 10 hours per week; or
    • be estranged from your parents/guardians; or
    • have been considered as statutorily homeless and qualified for assistance under your local authority’s ‘main homelessness duty’; or
    • belong to an ethnic group under-represented at Oxford (Black or Mixed Black, Bangladeshi or Mixed Bangladeshi, and Pakistani or Mixed Pakistani); or
    • be from a low-income background and in receipt of more than the minimum levels of support detailed from your regional funding body (see below to find out how to check whether you meet this financial criterion).

The Checking your eligibility tab of our Graduate Access Programme Application Guide, provides further details about how to ensure that you meet all the requirements. This includes:

ESRC-funded placements in Social Sciences

In addition to meeting the UNIQ+ eligibility criteria, to be considered for a placement with ESRC funding, you will need to be in the middle years of your first-degree studies and expected to obtain at least an upper second class (2:1) UK honours degree. These placements can only be undertaken in projects in the social sciences that are eligible for ESRC funding, which will be indicated in the project description where applicable.

Who should apply?

We are looking for proven and potential academic excellence. Applicants would usually be on track to achieve or have achieved a final undergraduate degree grade of a strong 2:1 or First, in a subject area related to the listed projects that you are interested in. If your transcript shows year on year grade progression towards the upper range of a 2:1 or above, then we’d encourage you to apply for the programme.

We encourage students at non-Russell Group institutions to apply. You can check if your institution is a member of the Russell Group.

You can find out more about the criteria we will take into consideration when selecting applicants in our Application Guide for Graduate Access Programmes. These include academic merit and potential, socio-economic information, contextual information, and gender for projects in certain subject areas.

The aim of the programme is to provide research opportunities for individuals from groups that are currently underrepresented in postgraduate research who have not yet been able to experience a period of substantial research. Places will be offered to those who will most benefit from the programme.

Should I apply for UNIQ+ if I have applied or plan to apply in 2022 for a PhD/DPhil course? 

As graduate admissions are still underway at most institutions for next academic year, you may not know the outcome of any PhD/DPhil application you have made/intend to make. We recommend you apply to UNIQ+ if you meet the criteria, but if you are then successful in your application for a PhD/DPhil, please let us know so we can withdraw your application.

How to apply for entry in 2022

We encourage applications from talented individuals who would find continuing into postgraduate study a challenge for reasons other than academic ability. Please ensure that you meet all the eligibility criteria, including the requirement to be ordinarily resident in the United Kingdom.

If you wish to apply for our internship programme, but have personal commitments (eg childcare or other caring responsibilities) that make it difficult to be in Oxford for the full six weeks, we would still encourage you to apply. If you are offered a place on the programme, we will discuss with you possible alternative options to undertake the internship depending on your circumstances and the type of project.

Completing the application form

To apply, you will need to complete our online application form. You will be required to provide information about your education, submit supporting documents, state your subject interests, provide a personal statement, and nominate a referee. Our Application Guide for Graduate Access Programmes provides full instructions for completing the application form.

As part of your application, you will be able to select which of our graduate access programmes you would like to be considered for (UNIQ+ internships and/or Wellcome Biomedical Vacation Scholarships).

You may apply to both programmes if you wish and you only need to submit one application. However, you will need to ensure that you meet the eligibility criteria for each programme you have selected because your application will only be considered for programmes that you are eligible to participate in.

Applications for the 2022 programme have now closed. If you have applied for the 2022 programme, you can still view your submitted application and find out what happens next.

Please submit your application as early as possible (preferably, at least one week before the deadline).

Only complete and eligible applications that are submitted by the deadline will be considered. For your application to be considered complete, your application will need to include all required documents and your referee will need to have submitted their reference by 12:00 midday UK time on Friday 18 February 2022.

The Guidance for referees section of our application guide will provide your referee with advice about what to include in their reference. For convenience, you may wish to provide your referee with the following web address: http://www.graduate.ox.ac.uk/access/referee

What happens next?

Please consult our Application Guide for Graduate Access Programmes for further information about how your application will be assessed, including information about how and when you can expect to hear the outcome.

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