Research Projects and Researchers available for new student applications
Applicants to our PhD courses may be eligible for School of Clinical Medicine managed PhD studentship funding (as well as the University funding competition). Unlike the University funding competition there is no separate section within the application portal to request nomination for Clinical School managed funding. The Department will assess all offer holders and nominate candidates for Clinical School managed funding based on the criteria of the specific schemes and the strength of the applications.
If you are nominated for DTP-MR or MRC iCASE funding, you will be required to attend an additional interview organised by the School. Further details will be provided if you are nominated and shortlisted for an interview. Prior to any nomination, your department will contact you to confirm that you have no objections to being considered for School funding.
To apply for any of the listed projects, please first contact the relevant Principal Investigator (PI). Do not apply until the PI has confirmed that you should proceed. Once you have their confirmation, you can submit a formal application.
Neurodevelopmental Research Group
Principal Investigator: Dr Varun Warrier vw260@cam.ac.uk
We are a research group focused on understanding neurodevelopment, as well as neurodevelopmental and mental health conditions, using large datasets, genetics, and cutting-edge analytical methods. We currently offer several research projects for interested PhD and MPhil students:
1. Genetics of Brain Structure and Function, and Links to Mental and Neurodegenerative Health Outcomes
This project investigates how genetics influence brain structure and function using MRI data from various sources and advanced data science techniques, including machine learning and artificial intelligence. Key research areas include:
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How genetics shapes the organisation and development of the brain
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Changes in brain structure and function across the lifespan
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Associations between brain characteristics and mental or neurodegenerative health conditions, with a focus on identifying potential therapeutic targets or interventions
Key publications:
2. Heterogeneity Among Neurodevelopmental Conditions and Links to Mental Health
This project explores the genetic and clinical diversity within neurodevelopmental conditions and how these relate to mental health outcomes. Key research questions include:
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What is the genetic heterogeneity within autism spectrum conditions?
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How are autism, ADHD, and other rare neurodevelopmental disorders related at the genetic level?
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Can we predict outcomes in individuals with autism using genetic and clinical data?
Key publications:
3. Neurodevelopment and Mental Health Across the Lifespan
This project aims to model how genetic and environmental factors influence the development of mental health and neurodevelopmental conditions over time. The research focuses on:
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How genetic risk contributes to the age of onset and diagnosis of these conditions
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Modelling the co-occurrence of mental health and neurodevelopmental disorders to identify meaningful subtypes
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Predicting mental health outcomes using combined genetic and environmental data
Key publications:
Find out more: www.neurodevelopmentalresearch.group
Data Science approaches to Psychosis and Depression
Principal Investigator: Dr Emanuele Osimo efo22@cam.ac.uk
We are involved in several research themes aimed at improving understanding and clinical management of mental illnesses such as depression and psychosis. These are some of the PhD themes available:
1. Clinical Risk Prediction Modelling
We are developing a clinical risk prediction tool called MOZART, designed to estimate the risk of treatment-resistant schizophrenia at the time of a first episode of psychosis. The model will be enhanced by integrating genetic predictors, aiming to improve early identification and personalised treatment strategies.
Relevant publication: Nature Mental Health (2023)
2. Epidemiology
This research focuses on the use of large population-based datasets to explore the origins and early risk factors of mental illnesses such as depression and psychosis. The aim is to identify modifiable risk factors that could inform early intervention and prevention strategies.
Example study: BMJ Mental Health (2023)
3. Electronic Health Records (EHR)
We use electronic health records to uncover patterns linking inflammation, cardiometabolic changes, and mental illnesses like depression and psychosis. This includes studying pharmacological outcomes, such as how patients respond to treatments, and identifying potential biological markers of disease progression or treatment resistance.
Example study: Brain, behavior, and immunity
4. Genomics
This work investigates the role of polygenic risk scores (PRS) and rare variant burden scores in mental illnesses. The focus is on understanding genetic contributions to conditions like schizophrenia and depression, and how these scores can be applied in clinical risk prediction and stratification.
5. Genetic Epidemiology
We apply methods such as Mendelian Randomisation to explore potential causal relationships between risk factors (e.g., inflammation, metabolic traits) and mental health conditions. These techniques help disentangle correlation from causation in complex psychiatric disorders.
Example study: Preprint (2024) [https://www.researchsquare.com/article/rs-4313341/v1]
Find out more: https://neuroscience.cam.ac.uk/member/efo22/
Join a PhD Programme in Clinical Informatics and Psychiatry at Cambridge – including industry collaboration
We are inviting applications for a fully funded PhD studentship as part of the prestigious MRC iCASE programme, led by Dr Emanuele Osimo at the University of Cambridge. This unique opportunity combines advanced data science, genomics, and psychiatry to address pressing clinical challenges in mental health. Co-supervised by Professor Graham Murray and including industry partner Akrivia Health, the project will explore clinical risk prediction, epidemiology, and electronic health record (EHR) mining using the UK’s largest repository of psychiatric EHRs, enriched with multi-omics and primary care data.
Potential projects include enhancing the MOZART tool for predicting treatment-resistant schizophrenia by integrating genetic data, conducting large-scale epidemiological studies to investigate the origins of mental illness, and mining EHRs to uncover links between inflammation, cardiometabolic changes, and psychiatric outcomes. Students will use cutting-edge natural language processing (NLP) techniques to extract structured data from clinical notes, and apply statistical modelling, machine learning, and genomics to develop clinically relevant insights.
The successful candidate will gain hands-on experience across academia and industry, including a placement with Akrivia Health’s R&D team. Training will span biomedicine, coding, statistics, and patient/public involvement, with access to Cambridge’s world-class research environment and resources. This studentship offers a transformative platform for developing impactful, clinically relevant research and launching a career at the intersection of psychiatry and data science.
Clinical Informatics and Data Science in Psychiatry
Systems Neuroscience Lab
Principal Investigator: Professor Petra Vertes pv226@cam.ac.uk
We offer a variety of research projects for MPhil and PhD tailored to students' interests and strengths. Our work focuses on using computational modelling and concepts from applied mathematics and network science to investigate brain function and dysfunction across the lifespan, with a particular emphasis on neurodevelopmental disorders.
We take an integrative approach, combining data across different modalities, scales, and species, and collaborate extensively with both theoretical and experimental research groups worldwide.
Our ongoing projects span a wide range of areas, including:
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Human and animal neuroimaging
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Single-cell and bulk transcriptomics
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Multi-electrode array recordings in cerebral organoids
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Computational modelling of spiking neural networks
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Neuropeptidergic signalling networks in invertebrate models
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And more
This interdisciplinary work aims to deepen our understanding of the biological basis of neurodevelopment and its disorders, using cutting-edge methods across neuroscience and computational biology.
Find out more: https://systems-neuro-lab.github.io/