UNIQplus projects

Primary supervisor

Professor Lindsay Baker

Project description

Bacteria live in complex environments and communities. In order to successfully navigate this, bacteria must be able to communicate across space. One way in which this is done is via extracellular vesicles (bEVs), particles that are released from cells and possess both membrane and soluble components. In this project, we aim to understand how the proteins trafficked in these vesicles influence community behaviour and ability to persevere through antimicrobial stress. This will be accomplished through a combination of structural biology and microbiology techniques. Upon isolation of bEVs from antibiotic-treated cultures, we will analyse the morphologies of bEVs via electron cryotomography. Additionally, we will probe the biological action of these isolates on healthy bacterial cells.

Project outcome:

You will gain experience in aseptic microbial culture and harvesting and purification of extracellular vesicles. You will have the opportunity to prepare samples for electron cryotomography and mass spectrometry, as well as participating in the data collection and processing for the former.

Entry requirements

You should have, or be studying, a molecular life sciences degree, such as Biochemistry or Microbiology. Familiarity with bacterial culture would be useful.

Primary supervisor

Professor Francesco Licausi

Project description

This research project aims to design and generate a synthetic chloroplast genome for potato (Solanum tuberosum) to enhance photosynthetic efficiency and resilience under high light and heat stress conditions. Climate change increasingly exposes crops to environmental extremes, leading to significant reductions in yield. By integrating advanced synthetic biology, genomics, and bioinformatics approaches, this project will redesign key chloroplast genes and regulatory elements involved in photosystem stability, photoprotection, and carbon fixation. The synthetic genome will be assembled and introduced into potato chloroplasts using targeted transformation techniques. Physiological, biochemical, and transcriptomic analyses will assess improvements in photosynthetic performance, thermotolerance, and growth under stress.

This work will establish a framework for chloroplast genome engineering as a sustainable strategy to improve crop productivity and climate resilience. Ultimately, the project seeks to provide a proof of concept for synthetic organelle genomes as tools for next-generation crop improvement.

Project outcome:

You will gain hands-on experience in synthetic biology, molecular cloning, and chloroplast genome engineering. You will develop skills in bioinformatics tools for genome design, sequence optimization, and comparative genomics. You will learn laboratory techniques such as DNA assembly, transformation, and plant tissue culture, as well as physiological and molecular assays to evaluate photosynthetic performance under stress. Additionally, you will gain experience in data analysis, scientific documentation, and interdisciplinary collaboration. This project will provide a strong foundation in plant biotechnology and equip you with valuable skills for careers in genetic engineering and crop improvement research.

Entry requirements

You should have, or be studying, a degree in Biology or Biochemistry. Knowledge of plant physiology would be advantageous.

Primary supervisor

Professor Gail Preston

Project description

Current climate models project a global average increase of air temperatures with more frequent temperature extremes accompanied by changes in precipitation events across the globe. Recent extreme weather events have been linked to severe local outbreaks for various plant pathogens. Plants and pathogens interact in complex ways, with the outcome of plant-pathogen interactions determined by various factors including both environmental factors and host and pathogen genetics.

In this project you will study how elevated temperatures and other abiotic factors affect virulence gene expression in economically important bacterial plant pathogens such as Pseudomonas syringae, using luminescent and fluorescent bioreporter systems that allow us to monitor virulence gene expression in vitro and in planta.

Project outcome:

You will gain hands on experience of microbiology, molecular biology and imaging techniques and associated data analyses. You will be fully integrated into the research group during your internship and participate in and present in lab meetings. If any aspect of your work is included in a future publication, you may be included as a named co-author on that paper.

Entry requirements

You should have, or be studying a science-related degree, such as Biology, Biochemistry, Chemistry or Physics. Some knowledge of microbiology or cellular processes could be useful.

Primary supervisor

Professor Paul Jarvis

Project description

Chloroplasts are organelles in plant cells that capture light energy for photosynthesis, and their function depends on thousands of proteins imported from the cytosol. Protein import is controlled by large multi-protein complexes in the chloroplast outer (TOC) and inner (TIC) membranes, but we don't yet fully understand how these complexes assemble to carry out this function.

The project will focus on exploring how proteins of interest, OEP80 and AKR2A, help assemble the TOC complex. This will be achieved using fluorescent tags that allow us to visualise how these proteins interact with components of the TOC complex and where in the cell these interactions occur. Experiments will include plant cell isolation, protein expression, and visualisation by fluorescence microscopy. A better understanding of how the TOC complex is assembled will be a fundamental step in developing approaches aimed at improving plant growth and development under ever increasing environmental and anthropogenic stress.

Project outcome:

You will be immersed into the research project and gain experience in a wide range of topics, from managing a small research project, developing interdisciplinary skills in the lab, receiving training in core techniques such as plant cell isolation, protein expression in plants, SDS-PAGE and immunoblotting, and confocal fluorescence microscopy, analysing and troubleshooting results, applying statistical analyses to make your conclusions more meaningful, and developing materials (figures, presentations and report) and skills to communicate your results to others. If your results make a significant contribution to a future publication, you will be included as a co-author in recognition of your contribution to the field during your time with us.

Entry requirements

You should have, or be studying, a degree related to Biological Sciences, Molecular Biology, Cell Biology, or Biochemistry with a strong interest in plants. Having some lab experience and the ability to work independently will be useful, although any required training and support will be provided.

Primary supervisor

Dr Mahan Ghafari

Project description

This project will explore how SARS-CoV-2, the virus causing COVID-19, spread globally in the early stages of the pandemic using publicly available viral genome sequences. You will learn how to assess sequence quality, align genomes, and place them on a phylogenetic tree to see how samples cluster by time and place. By examining these clusters, we will infer patterns of introduction, early transmission, and how new mutations emerged and rose in frequency. The project will use computational tools common in genomic epidemiology (eg sequence QC, multiple sequence alignment, tree visualisation) and will culminate in a short report on what the data reveal about early viral evolution and spread.

Project outcome:

You will learn how to work with real SARS-CoV-2 genomic data, including downloading public sequences, performing basic quality control, conducting evolutionary analysis, and exploring how sequences group on a phylogenetic tree. You will gain hands-on experience with core tools in genomic epidemiology (sequence alignment, tree visualisation, and simple mutation tracking) and learn how these analyses inform our understanding of viral evolution and global spread. You will also practice summarising genomic findings for a non-specialist audience and present your results to the group at the end of the internship.

Entry requirements

You should have, or be studying, a degree in a relevant subject such as Biology, Biomedical Sciences, Biochemistry, Genetics, or a quantitative discipline (eg Mathematics, Statistics, or Computer Science). Basic computer skills and an interest in working with data are encouraged, but prior experience in bioinformatics is not mandatory as training will be provided. A keen interest in viruses, infectious disease, or how SARS-CoV-2 evolves and spreads is essential. The project will suit a motivated student who is curious, comfortable learning new software, and able to work carefully with real-world data.

Primary supervisor

Professor Rob Salguero-Gomez

Project description

This project will explore patterns of flower colour and distribution in an invasive plant species using photographs submitted by the public through citizen science platforms. You will collect, organise, and analyse image data to investigate how flower colour varies across regions and whether environmental factors influence these patterns. You will gain hands-on experience in data management, programming, and the use of accessible artificial intelligence tools to help automate image labelling and analysis. The project also involves spatial data exploration to understand distribution trends. Through this work, you will develop transferable coding and research skills, deepen your understanding of invasive species ecology, and appreciate the value of citizen science in advancing environmental research and engaging the public in scientific discovery.

Project outcome:

You will gain experience in handling and analysing large citizen science image datasets, learning how to extract meaningful information about flower colour and distribution patterns. You will be trained in programming (using Python or R), data cleaning, image classification, and introductory machine learning methods to support automated labelling. You will also learn principles of programming reproducibility and how to create and maintain your own coding portfolio using GitHub.

By the end of the project, you will have produced a short written report and co-created a video explaining your findings. You will also present your results to the research group and may be acknowledged in future research outputs arising from the project.

Entry requirements

You should have, or be studying, a degree in Biology, Environmental Science, Geography, or a related quantitative subject such as Data Science or Computer Science. Some experience with data handling, programming (preferably in Python or R), and an interest in applying computational tools to ecological questions would be advantageous. Familiarity with image analysis, machine learning, or GIS software is desirable but not essential, as training will be provided. You should be curious, motivated to learn new analytical techniques, and comfortable working independently with guidance. An enthusiasm for biodiversity, citizen science, and communicating research to the public will help you get the most from this project.

Primary supervisor

Dr Joseph Poore

Project description

Changes in soil organic carbon (SOC) stocks on agricultural land can significantly influence CO2 emissions or removals. Adopting land management practices that increase SOC offers opportunities to reduce atmospheric CO2 and offset the carbon footprint of agricultural production. To better understand these effects, we are compiling the world’s largest harmonised archive of SOC data, linked to production practices and land-use history, enabling identification of drivers of SOC changes.

This project focuses on cacao and coffee systems globally, covering 11.7 and 12.2 million hectares, respectively, in 2023. Both crops are major drivers of deforestation in tropical regions, including Indonesia, Côte d’Ivoire, and Brazil. The internship will investigate SOC dynamics when forests are converted to plantations and assess how agroforestry can mitigate SOC losses compared with monocropping.

The main task is uploading SOC sampling data to the HESTIA platform, evaluating the effects of land management and land-use changes, with automatic calculations of associated CO2 emissions or removals and their broader life-cycle impacts.

Project outcome:

You will gain knowledge of the role of soil organic carbon sequestration in climate change mitigation efforts in the agricultural sector and how greenhouse gas emissions linked to agricultural activities are quantified and modelled using Life Cycle Assessment methods. You will be familiarised with cacao and coffee production systems and their broader environmental impacts. You will gain experience in conducting literature reviews, data mining and data analysis. You will also gain skills in Excel, Git and JSON schemas.

Entry requirements

You should have, or be studying, a degree in a quantitative discipline, such as Biology, Chemistry, Engineering, Environmental Science, Mathematics, Physics.

Primary supervisor

Dr Katie Dunkley

Project description

Fish live in social worlds where encounters with other species can bring rewards and/or risks. Some interactions benefit both sides, as when cleaner fish remove parasites, whereas others are harmful, involving species that feed on a fish’s mucus or scales. This project will investigate how fish use signals, such as body postures or chases, to influence the outcomes of these encounters in their favour. By analysing pre-collected video footage and/or conducting a laboratory experiment, it will explore how communication helps fish encourage cooperation, avoid harm, and navigate the challenges of living alongside other species. In doing so, the project will offer new insights into how animals use signals to manage conflict and cooperation, and how communication shapes social behaviour across species.

Project outcome

You will receive training in behavioural data collection and in how to code, analyse, and interpret animal signalling and interaction patterns using video and/or experimental data. You will also have the opportunity to develop practical skills in animal handling and fish husbandry within an aquatics laboratory. In addition, you will gain experience in designing decision-based experiments, formulating and testing hypotheses, and performing statistical analyses. There may also be opportunities to explore artificial intelligence approaches to data analysis.

By the end of the project, you will present a summary of your findings to other researchers in fish behaviour at an internal meeting. If your analysis contributes to a future publication, you will be included as a named co-author on that paper.

Entry requirements

You should be have, or be studying, a degree in a Biology-related discipline. A keen interest in behavioural ecology, communication, and/or social interactions in animals will be an advantage. Experience with data analysis in R and/or coding in Python would be helpful, but not essential as training and support will be provided.

Primary supervisor

Dr Irem Sepil

Project description

Aggression is common across the animal kingdom and winning contests helps individuals secure food, mates, or territory - key resources for their survival and reproduction. However, whether an animal should fight, how long for, and what determines who wins are fundamental questions we are still trying to answer. Female aggression, compared to males, has historically been neglected but there is growing evidence of the evolutionary significance of female-female competition.

Internal states are likely to influence an individual’s capacity and motivation to fight. For example, hunger may decrease an individual’s ability to win fights yet simultaneously increase their valuation of food as a resource worth fighting over. Precisely how food deprivation affects aggression is unclear. This is especially relevant for females, who typically fight over food resources. Therefore, this project will focus on understanding how food deprivation influences aggression in females.

Project outcome

You will be trained in key laboratory skills working with the model species Drosophila melanogaster, including setting up aggression assays as well as general fruit fly husbandry. You will also be trained in supervised machine learning approaches for behavioural tracking and video analysis in order to quantify aggression, as well as data analysis in R.

At the end of the project, you will use these skills to write a report and present your findings to the group. Results from this project may be used in future publications, and you would be included as a named author on such work.

Entry requirements

You should have, or be studying, a degree in Biology or a related discipline. You should have a strong interest in animal behaviour, ecology, or evolution.

Primary supervisor

Dr Joanna Bagniewska

Project description

School grounds have great potential for providing refuge for urban wildlife – yet are one of the most under-recorded urban environments in the UK. At the same time, there is a growing dissociation from nature, particularly notable among children and teenagers. While increasing numbers of schools are taking part in citizen science projects, such as National Education Nature Park, to map and monitor habitat and biodiversity on their grounds, not many of them have been assessed systematically by ecologists rather than citizen scientists.

This project aims to run biodiversity surveys across schools in Oxford to provide a baseline assessment, which can yield meaningful ecological data and be used as a benchmark for future outreach and public engagement projects. Information gained from this pilot will be shared with participating schools, as well as the Healthy Ecosystem Restoration in Oxfordshire network and the British Ecological Society.

Project outcome

By the end of the internship you will have:

1. gained experience in conducting biodiversity surveys across a range of taxonomic groups;
2. contributed to survey planning and setup; collected data contributing to a potential future publication, which you would be asked to co-author;
3. gained communication skills by presenting your findings to colleagues, teachers and students; and
4. gained project management skills and experience of working as part of a research team.

Entry requirements

You should have, or be studying, a Biology-related degree, and have an interest in ecology and conservation work. Some species identification skills would be very useful, although help will be provided in expanding these. You will be a self-starter with the ability to work independently.

You will be required to undergo a DBS check, since you will be working on school grounds.

Primary supervisor

Dr Yingxi Zhao

Project description

The UK’s health and care services face major challenges in recruiting and retaining sufficient staff. Currently, one in five NHS or social care workers in England is internationally recruited, a figure that is expected to rise. Private recruitment agencies are playing an increasingly central role in facilitating the international migration of health workers, particularly nurses, against the backdrop of a global workforce shortage. This internship, building on work by the Health Systems Collaborative team on UK and global health workforce issues, will involve a series of literature, policy reviews, and web-based content analysis to map recent policy changes, identify key actors, and update the evidence base on the experiences of internationally recruited staff.

Project outcome

Through this project, you will develop skills in conducting comprehensive literature and policy reviews, learn how to design search strategies and apply thematic coding, and gain experience of working with an applied health research team and applying analytical frameworks and theories.

We will expect you to produce a report or other relevant output to disseminate the findings - and to present this to colleagues in the Health Systems Collaborative team. If any aspect of your analysis is included in a future publication, you may be included as a named co-author on that paper.

Entry requirements

You should have, or be studying, a degree in Medicine, Allied Health, or Social Sciences (eg Education, Policy, Sociology, or Anthropology). Applicants from other disciplines are also welcome to apply, provided they can demonstrate a strong interest in social science or health services research, along with relevant skills such as literature and systematic reviews, and clear written and verbal communication.

Primary supervisor

Dr Makoto Saito

Project description

Clinical care in poorer countries often lacks strong supporting evidence, even though infectious diseases are the leading cause of morbidity and mortality in many of these areas. As part of the Infectious Diseases Data Observatory (IDDO)’s aim of enabling evidence-based decision-making to combat infectious diseases, our current project uses systematic reviews of existing literature to gather evidence regarding infectious diseases of poverty (such as malaria and neglected tropical diseases) in low- and middle-income countries.

We have extensive experience of such work, and have streamlined the evidence-gathering process, with our work feeding into the development of numerous clinical guidelines, including the World Health Organization’s malaria guidelines. We seek individuals interested in understanding these topics and the process of evidence synthesis. Expected activities include reviewing medical literature and summarizing findings by extracting relevant data. You may also have opportunities to contribute to report writing based on these findings.

Project outcome

You will gain a deeper understanding of the context (malaria, neglected tropical diseases or antimicrobial resistance) and develop skills in critically appraising published literature, an essential first step for many scientific projects, including defining a postgraduate project which clearly addresses a knowledge gap regardless of whether it involves wet lab or dry lab work. Working in collaboration with our team, you will also gain an overview of how to conduct systematic literature reviews in a scientifically rigorous way and synthesise existing evidence.

Entry requirements

You should have, or be studying, a degree in Biology or Medical Sciences. You should have a basic understanding of medical terminology and be competent in reading medical articles. No prior experience in statistical analyses or data management is required, although it can be helpful. The project involves no wet-lab work and primarily requires computer-based work.

Primary supervisor

Dr Christina Heroven

Project description

Receptor Tyrosine Kinases (RTKs) are proteins on the surface of cells that act like molecular switches, controlling important processes such as cell growth, development, and specialization. In cancer, RTKs are often mutated or become more abundant, leading to uncontrolled cell growth. Due to their involvement in disease, RTKs have become major targets for drug development.

In this project, we aim to explore a new way to inhibit cancer-promoting RTK activity by using the cell’s own regulatory system. Specifically, we plan to design a synthetic protein that brings an enzyme called a phosphatase to the target RTK, helping to switch off its cancer-driving signals.

Project outcome

You will be trained in basic molecular cloning and biochemical techniques. You will learn how to design, express and purify synthetic recombinant proteins. You will test the synthetic proteins for their anti-oncogenic properties using cancer cell proliferation assays and flow cytometry. You will also have the opportunity to characterize the molecules by biophysical methods. At the end of the project you will present your findings back to the group in an internal meeting.

Entry requirements

You should have, or be studying, a degree in Molecular Biology, Biochemistry, Biophysics, or Biological Sciences.

Primary supervisor

Dr Aram Swinkels

Project description

This project brings together two major health challenges of the 21st century: antimicrobial resistance and the widespread presence of microplastics.

Microplastics are now found almost everywhere, even in unborn foetuses. While they are believed to negatively impact human health, they may paradoxically provide advantages to bacteria. Microplastics have been detected in hospital sink drains alongside healthcare-associated pathogens, raising the question: how do bacteria and microplastics interact within the hospital environment?

In this project, we will perform in-vitro experiments to investigate how different bacterial strains behave when attached to microplastics. Using a diverse selection of materials from our in-house microplastics library, we will create a mock bacterial community for inoculation. You will perform growth experiments combining this community with various microplastics and assess resistance related parameters using classical, molecular, and computational microbiology approaches.

Project outcome

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

Entry requirements

You should have, or be studying, a degree in Microbiology or a related discipline. Previous laboratory experience in culture, DNA extraction and sequencing is desirable.

Primary supervisor

Dr Delaney Dominey-Foy

Project description

This project will explore how human immune cells interact and develop within complex three-dimensional tissue models. Using advanced lymphoid organoids, which are miniaturised systems that mimic the structure and function of human lymphoid tissue, you will investigate how T and B cells coordinate adaptive immune responses. You will gain practical experience in human cell culture, microscopy, and data analysis, contributing to ongoing efforts to establish next-generation organoid models for studying human immunity.

Project outcome

You will gain hands-on experience in human tissue culture, including the generation and maintenance of advanced lymphoid organoids. You will be trained to use confocal microscopy to image immune cell interactions and to analyse these images using AI- and machine learning–based tools. You will also perform and interpret multicolour flow cytometry to identify and quantify immune cell populations.

Alongside these technical skills, you will develop your ability to handle, visualise, and statistically analyse complex biological datasets. You will also gain experience in presenting your findings, both in group meetings and in a final research presentation.

By the end of the project, you will have a deeper understanding of human immunology and how adaptive immune responses can be studied using next-generation organoid models. If your data contributes to a future publication, you may be acknowledged or included as a co-author.

Entry requirements

You should have, or be studying, a degree in a relevant Biological or Biomedical Science, including courses that contain content on immunology, cell biology, biochemistry, or a related field. An interest in human immunology and experimental biology is essential.

Previous laboratory experience is helpful but not required as full training will be provided. Confidence in handling data and a willingness to learn computational approaches (for example, image or flow cytometry analysis) would be advantageous.

You should have good organisational skills, attention to detail, and enjoy working collaboratively as part of a research team. Curiosity, motivation to learn new techniques, and an enthusiasm for experimental discovery are the most important qualities.

Primary supervisor

Dr Alexander Davies

Project description

Neuropathic pain is a chronic condition that occurs because of injury or disease of the nervous system. It is thought to affect up to 10% of the adult population in the UK, yet diagnosis can be challenging. Identifying nerve damage in individuals with chronic pain would help tailor treatments and enhance our mechanistic understanding of the condition.

This project aims to develop small peptide molecules – shark antibodies - as biological tools to recognise injured sensory neurons in mice and humans. We will validate them as potential novel biomarkers for nerve injury.

You will be trained in wet-lab techniques relevant to the project at the time, which may include immunohistochemistry, enzyme-linked immunosorbent assay (ELISA), live cell-based assay and confocal microscopy.

In the longer term this work could help develop therapeutic interventions that target injured nerves specifically and avoid the side-effects of current medications for neuropathic pain.

Project outcome

You will be part of a small research team working in collaboration with an industrial partner employing proprietary technology to address this biological and therapeutic challenge. As well as being taught laboratory skills relevant to biological research more generally, you will learn how to design, execute and analyse research experiments. You will attend weekly lab and journal club meetings, as well as monthly project meetings with our industrial partner. At the end of your project you will have the opportunity to present your findings at a large group meeting.

Entry requirements

You should have, or be studying, a Biology or Biomedical Science-related degree with and have an interest in nervous system function in the context of health and disease. You will be able to input data into a spreadsheet and perform basic statistical analysis. You will appreciate the core principals of experimental design and scientific report writing. You will understand the basic principles of neural anatomy, antibody chemistry, and fluorescence microscopy. Experience in a biological laboratory is useful but not essential.

Primary supervisor

Dr Sam Webb

Project description

Aim: There is no one assessment that can accurately flag impairments in thinking skills following a stroke that:

1.  is suitable for any time post-stroke;
2. is suitable for different severity of strokes (mild to severe);
3. can account for different impairments in vision, attention; motor abilities, and communication; and
4. can be completed in a brief as time as possible.

Method: Administering neuropsychological assessments to stroke survivors who live in the community (in and around Oxfordshire), scoring assessments, entering data, and brief data analyses. Training in test administration and data curation/analysis will be provided.

Project outcome

You will be trained in neuropsychological assessment methods and scoring, as well as in data analyses using R. You will have the opportunity to contribute to lab meetings and gatherings during your internship. You will also gain experience in a translational neuropsychology research group, learning about the different types of research conducted and future pathways.

Entry requirements

You should have, or be studying, a degree in Psychology or closely related field, such as Neuropsychology. This project would suit someone who is interested in studying and/or progressing into clinical (neuro)psychology.

Primary supervisor

Professor Esther Becker

Project description

This project aims to understand how the brain’s immune cells, called microglia, help shape the development of the cerebellum, a key brain area that is important for motor control but also cognitive and emotional processes. We are using human induced pluripotent stem cells (iPSCs) to generate cerebellar organoids and will investigate how microglia and cerebellar neurons interact in this novel model system. Interesting questions that could be explored in the project include:

• Do microglia influence the development and maturation of specific neuronal subtypes in the cerebellum?
• How does the cerebellar microenvironment shape the identity and function of co-cultured microglia?

Lab techniques to address these questions will include tissue culture, molecular techniques such as qRT-PCR, and immunostaining.

There might also be the possibility to undertake functional analyses using multi-electrode arrays.

Project outcome

You will gain experience in experimental design, tissue culture, molecular and imaging techniques, and data analysis. You will have regular meetings with your supervisors to discuss your project and plan next steps. At the end of the project, you will present your findings back to the group at our lab meeting.

Entry requirements

You should have, or be studying, a degree in Biomedical Sciences or equivalent, such as Biology, Biochemistry, or Pharmacology. An interest in neuroscience would be advantageous. Training in all required techniques will be provided, but knowledge of experimental techniques and wet-lab experience would be useful.

Primary supervisor

Professor John Dawes

Project description

Neuropathic pain arises from damage to the nervous system and is associated with overactive neurons in the dorsal root ganglia (DRG) and spinal cord. Current treatments are often ineffective, highlighting the need for new approaches. Leucine-rich glioma-inactivated 1 (LGI1) is a secreted protein that regulates neuronal excitability (Fels et al., 2021). Autoantibodies against LGI1 have been linked to pain syndromes, and reducing these antibodies improves symptoms (Gadoth et al., 2017; Ramanathan et al., 2021). Using adeno-associated virus (AAV9) gene delivery, we increased LGI1 expression in a mouse model of nerve injury and found reduced pain sensitivity (Farah et al., 2024).

To understand how LGI1 works, we will use immunohistochemistry and confocal microscopy to study its expression and viral transduction in specific neuronal populations from spinal cord and dorsal root ganglia tissue. This project aims to identify LGI1’s mechanisms of action and evaluate its potential as a novel gene therapy for neuropathic pain.

Project outcome:

You will gain hands-on experience in neuroscience and molecular biology techniques, focusing on neuropathic pain mechanisms. You will be trained to handle and analyse experimental data from immunohistochemistry, learning to identify specific neuronal populations using confocal microscopy. You will also learn the principles of viral vector design and gene delivery, as well as how to interpret and present quantitative results using appropriate statistical methods. Additionally, you will gain insight into how preclinical models are used to investigate therapeutic strategies for nervous system disorders. The intern will present their findings in a lab meeting at the end of the placement and, if their work contributes to future publications, may be acknowledged or included as a co-author.

Entry requirements

You should have, or be studying, a degree in Neuroscience, Biomedical Sciences, Physiology, Pharmacology, or a related discipline. An understanding of basic neurobiology and molecular or cellular biology techniques would be advantageous. Prior experience with laboratory work would be useful but is not essential, as full training will be provided. You should have strong organisational skills, attention to detail, and enthusiasm for experimental research. The ability to work carefully, follow protocols, and communicate findings clearly will be important for success in this project.

Primary supervisor

Professor Simon Kyle

Project description

Wrist-worn actigraphy is commonly used in sleep research and clinical sleep medicine to estimate sleep and rest-activity rhythms. Actigraphy data are typically combined with patient-reported sleep diaries and manually scored by a clinician or researcher. However, the inclusion of actigraphy in large-scale cohort studies (involving 100,000+ participants) has led to the development of automated analysis approaches, enhancing efficiency and reliability. Nevertheless, it is unclear how sleep outcomes might differ between automated and manual scoring approaches.

In this project, we will explore how sleep parameters may differ when data are manually scored, with the aid of a sleep diary, compared to outcomes from automated scoring. You will set-up actigraphy devices, collect data using these devices, and then analyse how the data compares across scoring methods. You will have the opportunity to learn more about sleep research methods, how to score sleep data, and plan and implement your own data analysis plan.

Project outcome

You will have the opportunity to learn about a range of wearable and nearable technologies used within sleep research. The focus will be on analysing data from a wrist-worn actigraphy device, and investigating how sleep parameters differ across manual and automated scoring. You will also have the opportunity to learn about polysomnography, regarded as the ‘gold standard’ for sleep research, and learn how to set-up and score sleep polysomnography data.

You will be expected to explore a wide range of literature around actigraphy and its scoring, collect your own data, create and implement a statistical analysis plan, and write a report of what you find. Additionally, we would like you to present your findings at a lab meeting.

Entry requirements

You should have, or be studying, a degree in Neuroscience, Psychology, or a related discipline. This project will suit someone with an interest in sleep and/or wearable technologies. Experience with data analysis is beneficial, but support and training will be provided if needed.

Primary supervisor

Dr Pao-Sheng Chang

Project description

Neuropathic pain is affecting 9% of people worldwide, but many promising pre-clinical models fail to be translated into effective treatments. To better investigate the paroxysmal pain in human, we have developed a bespoke humanised live neuron recording system to measure the level of spontaneous firing in human sensory neuron. The system is applied to understand the causal relationship between co-culture cells or inflammatory mediators with human sensory neuron by measuring the level of spontaneous neuronal firing. We can then relate the cell-based measurements to the paroxysmal pain in clinics for developing better pain treatments.

Project outcome

You will experience live neuron recording conduction and analysis whilst also gaining the practical experience in maintaining human iPSC-derived sensory neuron in culture dish apart from other fundamental wet-lab practices. We aim to build up your experience and confidence in the lab with providing guidance for future study and career trajectory.

In addition, as part of a multidisciplinary pain research group working on projects from the bench to the clinic, this internship will also offer the unique opportunity to spend time each week with clinical researchers within the group. You will have the chance to observe data collection techniques used for investigating the mechanisms underlying painful conditions in patients, and learn more about translational research as a bonus.

Entry requirements

You should have, or be studying, a degree in biological science, preferably neuroscience. You should have an understanding of fundamental wet-lab works (such as cell culture and imaging technologies).

Primary supervisor

Professor Arjune Sen

Project description

Epilepsy affects more than 50 million people worldwide, with a disproportionate burden in low- and middle-income countries where access to diagnosis and treatment remains limited. This project contributes to the development of a Global Epilepsy Research Database, which collates and categorises literature on epilepsy care including diagnosis, treatment and prevention.

Working very closely with colleagues in Medical Humanities and an interdisciplinary team across neuroscience, global health and clinical research, the project will also support the development of an associated blog platform that translates research findings into accessible, practice-relevant insights.

This internship will use literature review techniques and thematic analysis to map current evidence. Together, these activities will help highlight research priorities and support future work to strengthen epilepsy care in resource-limited settings. Projects will align with the interests of applicants and our current work on epilepsy in LMICs, culturally contextualised technology, medicines access and epilepsy in dementia.

Project outcome

You will gain experience in literature review, database organisation and science communications. You will also develop skills in interpreting and presenting research data. You will have the opportunity to contribute to outputs from the project, which may include online publications or scientific papers.

Entry requirements

You should have, or be studying, a degree in Neuroscience, Psychology, Medicine, Public or Global Health, Biomedical Sciences or a related discipline. Strong analytical and writing skills are essential. Familiarity with PubMed or Excel would be advantageous. An interest in neurological disorders and global health research is essential. You should be motivated, detail-oriented and able to work independently and collaboratively.

Primary supervisor

Dr Najib Sharifi

Project description

This project aims to develop a framework that combines a Monte Carlo Tree Search (MCTS) algorithm with generative machine learning models to design new materials with desired chemical or physical properties. Through this project, we will explore how reinforcement learning and generative AI can accelerate the discovery of functional materials.

Project outcome

You will gain hands-on experience with machine learning for scientific discovery, including training and applying transformer and GNN models, and implementing search algorithms such as MCTS. You will also develop practical skills in Python programming, data handling, and computational chemistry tools, as well as experience interpreting model outputs in a research context. You will present your findings to the group and may contribute to a research preprint or publication depending on project progress.

Entry requirements

You should have, or be studying, a degree involving machine learning, such as Computer Science or a related discipline. You should have familiarity with writing machine learning models from scratch in pytorch. Knowledge of materials science is not necessary.

Primary supervisor

Dr Martin Lett

Project description

In our current project, we isolate T-cell receptor (TCR) sequences and clone them into cell lines to evaluate their antigen recognition, mainly within the non-classical MHC context. This work aims to deepen our understanding of antigen presentation to MAIT cells and to identify T-cell antigens that are specific to cancer.

Project outcome

You will gain hands-on experience in key immunological and cell biology techniques. You will learn how to perform in vitro T-cell activation assays and maintain mammalian cell cultures under controlled laboratory conditions. You will also be trained to use spectral flow cytometry to analyse cellular phenotypes and immune responses in detail. You will acquire skills in bioinformatic analysis to interpret complex cytometry datasets and integrate them with experimental findings.

Entry requirements

You should have, or be studying, a degree in a relevant discipline, such as Biology, Biomedical Sciences, Biochemistry, Pharmacology, or a related discipline and have a basic understanding of immunology, particularly T-cell biology and antigen presentation.

The ability to follow experimental protocols and maintain accurate labs is essential. Laboratory skills such as sterile cell culture techniques, pipetting, and handling biological samples would be advantageous, but is not a requirement. You should be motivated to learn new techniques and engage with interdisciplinary approached combining wet-lab and computational work.

Primary supervisor

Dr Nicholas Provine

Project description

Human tonsil organoids have recently emerged as a promising model for studying vaccine-induced immune responses in vitro. Our group has validated this system for modelling responses to the adenovirus-vectored vaccine ChAdOx1 nCoV-19, better known as the Oxford–AstraZeneca COVID-19 vaccine, and identified key cytokine pathways involved in B and T cell responses. The next step is to determine how well this model reproduces the interaction between T cells and B cells, which is essential for classical antibody production in vivo.

In this project, you will manipulate T cell function in the tonsil organoid system using specific antibodies and assess the effect on B cell activation and antibody secretion. Immune responses will be analysed using flow cytometry and ELISA. This work will advance understanding of the tonsil organoid model and refine its use for studying vaccine-induced immunity.

Project outcome

You will be trained in fundamental immunology techniques, including cell culture using primary human lymphoid cells, flow cytometry for characterising immune cell activation, and ELISA assays for measuring antigen-specific antibody responses. You will also gain hands-on experience in experimental design, data recording, and data interpretation.

Through this work, you will develop practical laboratory skills and a deeper understanding of vaccine immunology. You will be encouraged to present your findings in a group meeting, and if your data contributes to a future publication, you will be acknowledged as a named co-author.

Entry requirements

You should have, or be studying, a degree in a biological or biomedical science subject, such as Immunology, Microbiology, or Biochemistry. An interest in immunology or infectious diseases would be beneficial. You should have good attention to detail, be willing to learn new laboratory techniques, and have a careful and methodical approach to experimental work. Prior laboratory experience is not required, as full training will be provided.

Primary supervisor

Dr Agne Antanaviciute

Project description

Necrotising enterocolitis is a severe intestinal disease affecting premature infants, where parts of the gut become inflamed and die. Despite medical advances, the cause remains poorly understood, making it difficult to diagnose and treat. In severe cases, the disease can progress rapidly, leading to holes forming in the intestinal wall that require surgery.

The Antanaviciute and Simmons labs, based at the Weatherall Institute of Molecular Medicine, investigate intestinal development and disease using cutting-edge techniques such as spatial transcriptomics, which reveal how genes are active in different parts of the gut.

This project offers a rare opportunity to analyse high-dimensional spatial data from human neonatal tissue – an invaluable and limited resource. You will learn to analyse and identify cellular changes that drive disease progression in premature infants with Necrotising Enterocolitis, which may contribute to identifying early biomarkers to improve diagnosis and treatment.

Project outcome

You will receive training in computational tools used to analyse high-dimensional spatial transcriptomics data – a cutting-edge approach recognised by Nature Methods as 'Method of the Year' in 2020. Although still in its early stages, this technology holds immense potential for uncovering complex biological processes. You will work within an interdisciplinary team of biologists, clinicians and computational scientists, gaining a holistic understanding of gut development and disease and clinical research pipeline. While this is a dry lab project, there may be opportunities to observe or contribute to molecular biology experiments if desired. If any aspect of analysis is included in future publication, you may be included as a named co-author.

Entry requirements

You should have, or be studying, a degree in Biology, Biochemistry, Biomedical Science, or a related discipline. As it is a fully computational project, some experience in R would be an advantage.

Primary supervisor

Professor Qiang Zhang

Project description

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

You will have the chance to observe real-world clinical MR scans at the Division of Cardiovascular Medicine, and access to computing facilitates at Oxford Big Data Institute.

Project outcome

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

Entry requirements

You should have, or be studying, a degree in Computer Science or Engineering. You should have experience in machine learning and coding in Python.

Primary supervisor

Dr James Grist

Project description

This project will focus on developing a computer simulation that can be used to describe, and more importantly optimise, metabolic imaging experiments in humans. You'll use data already present in the lab, as well as fundamental physics mathematics, to make this framework as true to real life as possible. You'll use Python or Matlab to code the project and be well-supported in your time here.

Project outcome

You will be trained in fundamental MRI physics, python, Matlab, and image processing. You will have the opportunity to engage in lab meetings and experience and understand the work being performed by other members of the lab. You will be involved in research discussions and the day to day life of the lab. You'll be able to present your work at lab meetings and gain experience in scientific presentation.

If completed, you will be able to publish this work.

Entry requirements

You should have, or be studying, a degree in Computer Science, Engineering, or Physics. Experience in Matlap or Python is essential.

Primary supervisor

Dr Karina Pombo-Garcia

Project description

Our gut ages, which has implications for the cellular organization of proteins involved in cell-cell adhesion. Cell-cell adhesions are like the building blocks of a house, and the proteins responsible for holding them together act as the glue that allows our organs to function. In this project, you will have the opportunity to visualize, at very high resolution (on the nanometer scale), the distribution and organization of cell adhesion proteins in a gut-like model called gut-organoids. We will use patient-derived organoids from individuals of different ages to map how these proteins assemble as we age.

You will learn and use super-resolution microscopy with fluorescence proteins to visualize them in human tissue. Additionally, you will get familiar with handling the organoids and observing how they phenotypically change over several days in culture. While not required, it would be ideal if you have some basic knowledge of coding, such as Python, to assist in processing the images.

Project outcome:

You will gain experience in several key cell biology techniques as well as being immersed in an active research environment. This will enable broadening of expertise, as well as help you derive a better understanding of studying and working in a research laboratory.

The project itself will allow for training in cell culture and aseptic techniques. You will also be trained on key super-resolution imaging microscopy STED, as well as gut organoid development and maintenance.

There will also be the opportunity to work in a lab with a range of experience from junior to senior scientists who are willing to advise and mentor. If successful, the data and resources generated will have a real-world impact on ongoing research with your contribution credited accordingly if work with their direct involvement is published.

Entry requirements

You should have, or be studying, a degree in Biology, Biochemistry, or Medicine. Basic understanding of cell biology and microscopy, alongside familiarity with Python, would be advantageous.

Primary supervisor

Professor Christopher Toepfer

Project description

Inherited cardiomyopathy affects 1:200 people. These conditions cause changes in heart function that lead to heart failure and sudden cardiac death. There are still many things to be understood about how these conditions drive the progress towards heart failure. A key signature that has yet to be studied is how extracellular vesicle (EV) release is altered in cardiomyopathy. We have the opportunity to use human stem cell derived cardiac cells that carry cardiomyopathy gene mutations to study changes in EV synthesis and release in the dish. This will inform our understanding of how these EV signatures of communication affect disease progression.

Project outcome:

You will measure EV abundance and characteristics that will be extracted from media that human stem cell derived cardiomyocytes are grown in. You will be able to assess differences between control cell EV profiles and those of cells carrying cardiomyopathy genes. At the end of the project you will have the opportunity to present the work that you have performed to the research group. If any aspect of your analysis is included in a further publication you may be included as a named co-author on that publication.

Specifically you will have the opportunity to work with stem cells a their use in producing cardiac cells of the heart. You will gain experience with EV extraction protocols and assays for assessing EV abundance and size.

Entry requirements

You should have, or be studying, a degree in Biology, Biomedical Science, or Biochemistry and have experience or an interest in wet lab science.

Primary supervisor

Professor Leanne Hodson

Project description

Liver disease, known as metabolic dysfunction-associated steatotic liver disease (MASLD) is an independent risk factor for heart disease. It is known that if you gain weight or have a diet high in saturated fat this can increase the risk of heart disease. It has emerged that having a diet where you don’t gain weight but is high in sugar, and low in saturated fat and alcohol, also increases a person’s risk for heart disease. It is unclear why this is but has been suggested to be due to a change in liver metabolism with the liver making particles that are more atherogenic.

To test this, this project will use well characterised in vitro cellular models that allow the exploration of how different amounts of sugar and fats alter the regulation of pathways that produce lipid-containing particles. This will be achieved by using cell culture techniques along with a combination of approaches, including, culturing cells with stable-isotope tracers (specially labelled molecules), and analysing cells and media using mass-spectrometry to determine how specific pathways, like fat synthesis are changed when different amounts of sugars are added, along with the analysis of molecules in the media using a biochemical analyser, and measuring changes in the expression of proteins (through western blots and PCR) in hepatocytes.

Project outcome

You will have the opportunity to gain experience and skills in working with specialised laboratory techniques that use stable-isotope tracers to follow the fate of sugars through metabolic pathways in liver cells and changes in biomarkers associated with cardiovascular disease. You will learn how undertake statistical test to determine if there are differences in how specific nutrients are metabolised in cell and there is the opportunity to contribute to other on-going experiments in the lab.

At the end of your project you will present your project and findings to the group at an internal lab meeting. If any of the findings you generate are included in a future manuscript, then you may be included as a named co-author on the paper.

Entry requirements

You should have, or be studying, a degree in Biochemistry or Physiology and have an understanding of the key metabolic pathways in humans.

Primary supervisor

Professor Audrey Gerard

Project description

This year's Nobel Prize in Medicine was awarded to the discovery of regulatory T cells (Tregs). Tregs are cells of the immune system that are making sure our body does not attack itself. They mainly do so by restraining other immune cells, called conventional T cells. Conventional T cells are important to fight infection, but they also have the capacity to attack our own body. Without Tregs, conventional T cells would go uncontrolled and attack our organs, leading to autoimmunity.

In the Gerard lab, we aim to understand how Tregs control T cells not to attack our self, while allowing them to fight infection. To do so, we study where both Tregs and conventional T cells are found and how they influence each other during infection. We believe that Tregs are segregated from the site of infection, which is important to allow conventional T cells that can recognise infection to get activated and kill infected cells.

In this project, you will shadow and participate to our effort to characterise the location and communication between Tregs and conventional T cells during infection using techniques such as imaging or flow cytometry. You will also learn how to generate Tregs.

Project outcome

You will learn cell culture and imaging and get a deeper understanding of the immune system.

Entry requirements

You should have, or be studying, a degree in Medicine, Biology, or Immunology.

Primary supervisor

Professor Mark Coles

Project description

This project will involve spatial transcriptomics of biopsies from patients with immune mediated inflammatory disease and relating the spatial gene expression to pain scores. The techniques include the application of a python package to analyse cellular segmentation and quantify the gene transcription on a single cell basis and then to use this information to quantify the structures in the tissues. The project will involve learning python and other data analytical techniques. The project aims to identify cells within the tissue biopsy, quantify gene expression in individual cells and then use this information to develop a map of cellular organisation and function and how this relates to pain.

Project outcome

You will undertake spatial data analysis of human tissue sections from inflammatory disease using with the aim of generating evidence for molecular pathways that might related to patient measured pain. The analysis will identify key cell types involved in pain and the phenotype of those cell types comparing patients with high pain to controls.

Entry requirements

You should have, or be studying, a degree in Mathematics, Physics, Computer Science, or another relevant discipline, and be interested in applying your skillset to a biological problem.

Primary supervisor

Dr Annika Jödicke

Project description

Understanding how well the new medicines for cardiovascular and metabolic disease work, and how safe they are in real-world settings is essential. This project will focus on 'real-world evidence' (RWE), referring to studies using data gathered in routine care settings, such as by general practitioners in primary care, or in specialist settings of hospitals. Systematic reviews of the literature provide a comprehensive overview of the published knowledge at a time. This is particularly important to identify gaps for future.

You will begin by developing a protocol for a literature review, including a search strategy and criteria for including or excluding studies. With guidance from supervisors, you will then review and discuss the identified studies with the supervisor team, and write a summary of the findings that will inform future research. This work will provide valuable experience in research methodology, with the potential to published as part of a project report or scientific manuscript.

Project outcome

You will have the opportunity to gain experience in conducting literature reviews using a systematic search strategy. We will support you in developing a protocol for the review, work with specialists at the Bodleian Libraries to develop a search strategy, teach you how to screen titles and abstract, and extract relevant information from the scientific publications we shortlist.

We aim to write a report on the literature review that will be published alongside the protocol, and, where possible, include the findings from the literature review for the introduction/discussion sections of future research publications or project reports.

Entry requirements

You should have, or be studying, a degree in a medical-related field, eg Public Health, Nursing, Pharmacy, Epidemiology, Medicine, or Statistics. An interest in reading medical literature and are interested in learning how to conduct literature reviews would be advantageous.

Primary supervisor

Dr Martí Català

Project description

Join a dynamic and multidisciplinary team in the Health Data Sciences group at NDORMS, University of Oxford. Our team comprises experts from various fields, including medicine, pharmacy, mathematics, statistics, and computer science, who collaborate to use real-world data (RWD) from hospitals, general practices, and disease registries to improve public health.

While essential for testing new treatments, clinical trials are conducted in controlled settings with carefully selected patient populations. This limits their ability to fully represent how treatments perform in the real world. By contrast, RWD reflects the broader population and real healthcare practices, offering insights that clinical trials might miss, such as unobserved side effects or how treatments are used in routine care. However, since RWD was not originally collected for research purposes, careful processing and analysis are required to ensure reliable scientific evidence.

In this internship, you will reproduce and characterise (such as analysing the survival of patients with a specific disease or comparing the effectiveness of a drug) a population of interest using RWD sources, including data collected from general practices, to answer research questions.

Project outcome

This internship provides comprehensive training in R programming, big data handling, clinical epidemiology, and biostatistics. You will have the opportunity to build your presentation skills by regularly presenting your work in team meetings, with potential for co-authorship on project publications. These skills are highly valued in both academia and industry (particular in pharmaceutical related fields), providing a solid foundation for future job applications and career development.

Entry requirements

You should have, or be studying, a STEM-related degree. Familiarity with R programming is desirable.

Primary supervisor

Dr Yang Luo

Project description

This project explores how genetic variation influences gene activity at single-cell resolution. Using data from a newly generated single-cell study, you will investigate how genetic differences shape gene regulation across cell types. Possible directions include visualising genetic associations, linking variants to regulatory mechanisms, or applying simple AI or machine-learning approaches to identify patterns in the data.

The project will use computational and bioinformatics tools such as R, Python, and online genomic databases. It offers hands-on experience with real genomic data and introduces how AI-driven analysis can enhance our understanding of gene regulation and its role in human disease.

Project outcome

You will gain hands-on experience working with genomic and single-cell data, using tools such as R, Python, and online databases for data analysis and visualisation. You will develop practical skills in bioinformatics workflows, data integration, and the use of basic AI or machine-learning methods to identify patterns in complex datasets.

By the end of the project, you will understand how genetic variation influences gene activity and feel confident working with large-scale biological data. You will present your findings to the research group, and if your analysis contributes to ongoing work, you may be acknowledged or included as a co-author in a future publication.

Entry requirements

You should have, or be studying, a degree in Mathematics, Biology or a related discipline. You should have a strong interest in genetics, genomics, or computational biology (although detailed knowledge of genomics or single-cell analysis is not required). Prior experience with programming or data analysis (for example, R, Python, or a similar language) would be advantageous. This project is ideal for students who are comfortable working with data and keen to apply or develop their coding skills in a biological research setting.

Primary supervisor

Dr Dario Carugo

Project description

Bone metastases from a primary tumour are one of the most common sites of metastases, yet exhibiting high rates of mortality and morbidity. Patients suffer from cancer-induced bone pain, limited mobility, debilitating fractures, depression, cardiac arrhythmias and have an increased caregiving burden with reduced quality of life. Current methods of treatment are limited in improving outcomes and the lived experience of these patients.

The aim of the project is to investigate the efficacy of combining ultrasound-responsive nanoparticles with chemotherapeutic or targeted anticancer agents in vitro, using relevant bone-metastatic cancer cell lines. These particles start oscillating when exposed to ultrasound, which helps releasing a therapeutic agent at the desired location and point in time. Key techniques employed will include microbubble synthesis and characterisation, nanoparticle conjugation, particle sizing, microscopy, and cell culture–based cytotoxicity assays to identify drug targets. Fundamental data analysis and plotting will be utilised to assess efficacy.

Project outcome

You will gain practical, hands-on experience across both engineering and biological research disciplines. You will develop an understanding of nanoparticle and microbubble fabrication and characterisation techniques, as well as an understanding of mammalian cell culture and cytotoxicity assays to evaluate therapeutic efficacy. You will generate and analyse datasets evaluating whether the developed nanoparticle/microbubble formulations induce biological effects on cells. You will have the opportunity to present your findings to a multidisciplinary research group in an internal seminar and discuss your experimental results in the context of ongoing work. If your data or analysis contributes to a future publication or conference proceeding, you will be included as a named author.

Entry requirements

You should have, or be studying, a degree in a scientific or engineering discipline, such as Biology, Biomedical Sciences, Engineering, or a related discipline. Prior experience with, or understanding of, cell culture and basic microscopy techniques would be advantageous, but is not necessary. An interest in biomedical device development, cancer therapeutics, or drug delivery systems is desirable.

Primary supervisor

Professor Claudia Monaco

Project description

Macrophages have a key role in health and disease, by supporting organ function and immune responses to damage and disease. In homeostasis, ontogeny and organ-specific signals influence the phenotype and function of macrophages by activation of specific transcription factors. Using single cell (sc) RNA sequencing (RNAseq) and mass cytometry (CYTOF) in human and murine arteries, we revealed the existence of significant transcriptional and spatial heterogeneity of vascular and perivascular macrophages at the steady state. Distinct subsets are identifiable within the intima and adventitia of the arterial vessels. The functional and physiological relevance of this heterogeneity is unclear, but it points to different physiological functions related to immunity and metabolism. We developed genetically modified strains designed to evaluate the role of specific macrophage subsets in whole organisms and their origin, and state of the art imaging techniques to interrogate their intercellular interaction.

Project outcome

You will analyse data from disease relevant and combined models, human biobanks, combined with spatial proteomics technologies such as mass cytometry (and the relevant computational tools) to map the local interactions of vascular macrophages, immune and stromal cells and how they contribute to vascular health.

By completing this project, you will gain insight into the acquisition and analysis of single cell biology datasets from murine and human arteries and validation of key selective macrophage markers for identification of macrophage geography in arteries.

Entry requirements

This project, dependant on the successful candidate’s interests, may be computational or wet lab based and all necessary training will be provided. For a computer-based project, you should have, or be studying a degree in Computer Science or a related discipline. For a wet lab-based project, you should have, or be studying, a degree in Biology, Immunology, or a relevant discipline.

Primary supervisor

Professor Teresa Lambe

Project description

Vaccines are among the most effective and cost-efficient public health interventions, protecting millions from infectious diseases each year. Establishing protective immune responses and the formation of adaptive immune memory following vaccination are crucial for ensuring long-lasting immunity. Professor Teresa Lambe’s research group at the Oxford Vaccine Group is currently developing and testing vaccines against several high-priority outbreak pathogens, including Ebola virus, Marburg virus, Crimean-Congo haemorrhagic fever virus, and coronaviruses.

This project offers an exciting opportunity for a student to work alongside senior researchers in the field to characterise vaccine-induced immune responses using key immune monitoring techniques such as ELISpot, ELISA and viral pseudoneutralisation assays to assess T cell and antibody responses to an outbreak pathogen. The findings will contribute to understanding vaccine immunogenicity and guide the design of effective vaccines for future outbreak threats.

Project outcome

You will be trained in general laboratory practices and immunological assays, as well as in assay-specific data analysis. At the end of the project, you will have the opportunity to present your findings to the group in an internal meeting. Through this experience, you will gain valuable insight into the vaccine development pipeline and the assessment of immune responses, while working alongside experienced researchers in vaccinology. This placement will strengthen your technical expertise, communication skills and ability to collaborate effectively in an academic research environment.

Entry requirements

You should have, or be studying, a degree in Biology or Biomedical Sciences, and have an interest in infectious diseases.

Primary supervisor

Dr Anna Rose

Project description

Our work studies the molecular pathways that drive cancers that affect children and young people, with a particular focus on brain and bone tumours. These cancers all have very poor outcomes, and currently there are no targeted therapies available. In our lab, we aim to understand the molecular pathways better, so that we can identify, design and test new potential therapies. In this project, you will test a range of repurposed and/or novel small molecule drugs in either bone or brain cancer cell lines. You will learn key skills in tissue culture (growing cell lines in the lab) and cell viability assays, which monitor the effectiveness of a drug. You will also gain exposure to a wide range of other molecular techniques, such as DNA, telomere and protein analysis. The project is ideal for someone interested in translational molecular biology.

Project outcome

You will develop skills in growing and testing a variety of cancer cell lines in tissue culture, as well as treating cells with chemotherapeutic agents. You will also learn techniques for measuring cell survival, such as MTT assay and clonogenic assay. This will include how to collect and analyse the data, including statistical analysis using appropriate software. You'll observe and assist with a variety of molecular biology experiments which are key to our research programme, including our labs special skills in telomere analysis. You'll attend our weekly lab meeting, which will include journal club presentations (where you can begin to learn about critical appraisal of published work) and data presentation - which we will support you in presenting at. If any aspect of your analysis is included in a future publication or conference presentation, you would be included as a named co-author on the work.

Entry requirements

You should have, or be studying, a degree in Biology or Biomedical Sciences, have an understanding of basic molecular concepts, and have an interest in translational cancer biology. Basic experience in laboratory wet work (eg use of pipettes and other basic equipment) is preferable but not essential.

Primary supervisor

Dr Anet Jorim Norbert Anelone

Project description

Measles outbreaks are tragic reminders that it is important to be vaccinated against measles to reduce the risk of infection, health complications, and death. Measles infects immune cells, predominantly B and T cells, and these cells themselves are subject to variation due to age or other infections such as HIV. It is of public health importance to advance understanding of factors influencing successful vaccination, eliciting a high level of measles-specific neutralizing antibodies.

This study will investigate the impact of age, HIV status, and immune cell counts on the outcomes of measles vaccination. In particular, this study will involve mathematical modelling and statistical analysis to estimate how age-dependent changes in the counts of different subsets of T and B cells influence the number and kinetics of measles-specific antibodies.

The results have the potential to inform decision-making for the administration of the first dose of measles-containing vaccines.

Project outcome

You will gain hands-on skills and coaching in scientific programming in R, as well as in the mathematical and statistical analysis of experimental and clinical data.

You will also gain knowledge of Good Clinical Practice (GCP), Information Security and Data Protection, as well as the standard operating procedures of the Oxford Vaccine Group, including those related to statistical analyses for clinical trials.

You will also have the opportunity to attend the weekly meeting of statisticians at the Oxford Vaccine Group, and present at the end of your internship. We would be pleased to include you as a co-author or acknowledge your contribution in a future publication, should any of your work with us be incorporated. You will also be invited to write a personal reflection on your experience, which may be published on the website or in the newsletter of the Oxford Vaccine Group.

Entry requirements

You should have knowledge or experience in at least one of the following areas: scientific programming (R, Python, Stata, SAS, Matlab, etc.), statistics, epidemiology, clinical trials, mathematics, machine learning, control engineering, bioinformatics, data science, infectious diseases, computational biology, vaccinology, immunology, biology, medicine, or any other relevant discipline.

Primary supervisor

Professor Stephan Sanders

Project description

This project explores how gene activity and regulation differ in the hypothalamus between autistic and non-autistic individuals using single-nuclei multiomic data, where both RNA expression and chromatin accessibility (ATAC) are measured from the same nuclei. The dataset includes 40 human donors with whole-genome sequencing, allowing us to explore how genetic variation influences transcriptional and regulatory networks.

In this project, you will identify cell types and pathways showing altered transcription or regulation, and relate these to genetic risk for autism. You will compare your findings with bulk RNA-seq from the prefrontal cortex (PFC), single-cell data from other brain regions (PFC, putamen, and Wernicke’s area), and published datasets. Recent work has shown that autism-associated genes are highly expressed in the developing hypothalamus and thalamus; this study will test whether similar or distinct molecular patterns occur in the hypothalamus, offering new insight into how gene regulation in the brain may shape neurodevelopmental differences.

Project outcome

You will be trained in cutting-edge statistical, bioinformatic, and genetic techniques for analysing single-nucleus multiomic data, integrating RNA expression, chromatin accessibility, and genetic variation. You will gain experience using computational tools to explore how these molecular layers interact and differ across cell types in the human brain. The project offers the opportunity to work alongside leading experts in autism genetics and neurogenomics, within a research environment also focused on developing therapies for rare disorders. At the end of the project, you will present your findings to the group in an internal meeting. If your analysis contributes to future publications, you may be acknowledged as a named co-author.

Entry requirements

You should have, or be studying, a degree in Natural or Life Sciences, Statistics, Mathematics, Computer Science, or a related discipline. Familiarity with R and/or Python is required, and experience with data analysis or visualisation would be advantageous. An interest in genomics, neuroscience, or gene regulation would be beneficial, as the project involves interpreting biological data in a quantitative context. You should be motivated to learn new computational approaches, have good attention to detail, and be comfortable working independently as well as collaboratively within a research team.

Primary supervisor

Professor Pedro Carvalho

Project description

Accumulation of misfolded proteins and aberrant protein aggregates are hallmarks of a wide range of pathologies such as neurodegenerative diseases and cancer. Under normal conditions, these potentially toxic protein species are kept at low levels due to a variety of quality control mechanisms that detect and selectively promote their degradation. Our lab investigates these protein quality control processes with a particular focus on ER-associated degradation (ERAD), that looks after membrane and secreted proteins.

The ERAD pathway is evolutionarily conserved and in mammals, targets thousands of proteins influencing a wide range of cellular processes, from lipid homeostasis and stress responses to cell signalling and communication. We investigate the mechanisms of ERAD using multidisciplinary approaches both in human and yeast cells. We are using CRISPR-based genome-wide genetic screens and light microscopy experiments to identify and characterize molecular components involved in the degradation of disease-relevant toxic proteins. In parallel, we use biochemical and structural approaches to dissect mechanistically the various steps of the ERAD pathways.

These strategies helped us in discovering ERAD mechanisms contributing to the homeostasis of the endoplasmic reticulum, the organization of the nuclear envelope and regulation of lipid metabolism. Although we focus primarily on fundamental aspects of protein quality control, our work will shed light on how these processes are disrupted in human disease and may ultimately contribute to better therapeutics.

Project outcome

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

Entry requirements

A good background in Biology is desired, therefore, applicants must have completed at least an A-Level in Biology to be eligible for this project.

Primary supervisor

Professor Jordan Raff

Project description

Centrosomes are complicated protein machines that play an important part in organising eukaryotic cells. If human cells lose their centrosome, they usually kill themselves, and centrosome dysfunction has been linked to a plethora of human diseases - including cancer and microcephaly. 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 cells expressing fluorescently-tagged versions of the key proteins that drive centrosome assembly. These large imaging datasets will be analysed using computational methods to track how the centrosomes grow and divide and how individual proteins behave. These quantitative measurements are allowing us to better understand how centrosome growth and division are regulated during cell division and development, providing important insight into how these processes go wrong in disease.

Project outcome

You will be trained in Drosophila genetics (setting up crosses to generate living fly embryos for analysis and injection), advanced microscopy (using several different types of sophisticated microscopes), and you will learn how to analyse large imaging datasets with various computational tools. You will also have a chance to learn some molecular biology (DNA cloning, mRNA preparation) and biochemistry (protein purification). You will attend our weekly group meetings and be expected to present your work to the Group at the end of your Project.

Entry requirements

You should have, or be studying, a degree in a relevant discipline, such as Biological or Physical Sciences, Mathematics, or Engineering. An interest in understanding how complex biological systems work is essential. All training will be provided.

Primary supervisor

Professor Omer Dushek

Project description

T cells are important white blood cells that orchestrate immune responses. They use specialised receptors, called T cell receptors (TCRs), to recognise specific molecular signatures on these targets. Understanding how T cells make these recognition decisions is crucial for improving vaccines and immunotherapies.

This project aims to develop a new system to study how naïve (unactivated) human T cells respond when they are given defined TCRs. You will introduce selected receptors and other cell signalling molecules into T cells using a technique called nucleofection and then measure how the cells react using cell culture, flow cytometry, and molecular biology approaches. The goal is to create a reliable platform to study early T cell activation, helping us understand how immune responses begin and how they could be better controlled in disease and therapy.

Project outcome

You will be trained in and have the opportunity to conduct molecular cloning, eukaryotic cell culture, and flow cytometry experiments. You will also gain experience in analysing flow cytometry data, gel images and fitting simple mathematical models to your data. You will produce figures of your data and present your findings at a friendly internal group meeting at the end of the project. The research findings may be included in a future research study.

Entry requirements

You should have, or be studying, a degree in Biochemistry, Molecular or Cellular Biology, or a related biomedical area; and have an interest in molecular immunology research.

Primary supervisor

Dr Sally Cowley

Project description

Microglia are strongly implicated in the progression of Alzheimer’s, and neuroinflammation is also a feature of other neurodegenerative diseases, including Parkinson’s and ALS. We have developed a genetically tractable system for differentiating authentic human microglia from induced Pluripotent Stem cells, which is used widely to investigate disease pathogenesis and identify new therapeutic targets.

You will join our team who are using human iPS-microglia to help investigate how the Alzheimer’s-associated aggregation-prone protein tau is taken up by microglia, how it is trafficked in these cells, and how it is released from microglia in potentially more toxic forms, including in extracellular vesicles. You will focus on one aspect of this pathway, applying relevant assays to, for example, quantify tau in specific subcellular locations.

Project outcome

You will learn human iPS cell culture and differentiation, isolation of subcellular fractions and/or extracellular vesicles, immunocytochemistry with associated image analysis, and cellular assays for detection of tau.

Entry requirements

You should have, or be studying, a degree related to Biomedical Sciences. Knowledge of neuroscience and/or immunology would be advantageous. Familiarity with tissue culture techniques would be useful but is not essential.

Primary supervisor

Dr Anthony Roberts

Project description

The goal of this project is to investigate how motor proteins transport cargo in eukaryotic cells, while obtaining training and experience in a variety of molecular and cell biology techniques. Our research group specialises in the motor proteins kinesin and dynein, which use ATP hydrolysis to move along microtubules. These motors transport a range of macromolecular cargo in diverse physiological processes, underscored by the severe human disorders that arise from their dysfunction.

To better understand their mechanisms of movement and regulation, we solve cryo-EM structures of kinesin and dynein complexes. From these structures we generate hypotheses, which we test by designing and introducing mutations into cultured cells using CRISPR-Cas9 genome editing. We then visualise the movement of the motors and their cargoes in mutant and wild-type cells, tagging the proteins with bright fluorescent proteins and observing their motility using advanced fluorescence microscopy. In this project, you will use these methods to generate a novel mutant and characterise its behaviour.

Project outcome

You will be trained in and have the opportunity to conduct molecular cloning, eukaryotic cell culture, and fluorescence microscopy experiments. You will gain experience in analysing sequencing data, gel images, and fluorescence microscopy time-lapse videos. You will produce figures of your data and present your findings at a friendly internal group meeting at the end of the project.

Entry requirements

You should have, or be studying, a degree in Biochemistry, Molecular or Cellular Biology, or a related discipline.

Primary supervisor

Professor Matthew Freeman

Project description

We study membrane proteins and how they control signalling and cellular responses to stress. These processes are implicated in multiple human diseases including cancer, neurodegeneration, inflammation and infection so, although we mostly do discovery science, our work has wide potential medical relevance, and we are also interested in the translational opportunities.

Our particular focus is the rhomboid-like superfamily. We were the first to discover rhomboids, and we proved that they were novel intramembrane proteases, conserved across evolution, and that they controlled growth factor signalling.

More recently we have become interested in the much wider superfamily of rhomboid-like proteins, the majority of which are not proteases. Of these non-protease rhomboid-like proteins, we especially focus on the iRhoms, which we discovered to be primary regulators of inflammation.

Our experimental approaches include genetics, cell biology, biochemistry and structural biology, mainly in mammalian cells but also with a variety of model systems.

Project outcome

You will gain experience with experimental cell and molecular biology, as well as participating in a research group focused on discovery science. By the end of the project, you should have completed some aspect of one of our projects. If what you do is included in a future publication you may be included as a co-author.

Entry requirements

You should have, or be studying, a mechanistic bioscience-related degree, such as Biochemistry, Biomedical Science, or Biology.