Understanding Mental Health and Behaviour Change Using UK Biobank Data

mental health trajectories.

UK Biobank provides a major resource for addressing these questions through its large-scale data on mental health, lifestyle, sleep, physical activity, social circumstances, physical health, biological measures and, for some participants, wearable-derived behaviour. Drawing on perspectives from mental health research and behavioural data science, this project will investigate patterns, predictors and trajectories of mental health and behaviour change. Possible topics include relationships between mental health and sleep, physical activity, social behaviour and lifestyle; factors associated with improvement or deterioration in mental health; and the identification of groups following different mental health and behavioural trajectories.

The student will undertake the complete quantitative research process. This will include reviewing relevant mental health and behavioural literature, formulating research questions, identifying suitable UK Biobank variables, contributing to the data-access application, developing analysis plans, constructing reproducible datasets, and implementing and interpreting appropriate quantitative methods.

Possible approaches may include longitudinal and multilevel modelling, latent variable and trajectory models, survival analysis, causal-inference methods, and suitable machine learning techniques. Particular attention will be given to missing data, confounding, selection bias, measurement uncertainty, individual differences and the distinction between prediction and explanation.

We invite proposals that use UK Biobank data to address important questions concerning mental health and behaviour change. The precise topic and methods can be refined according to the applicant’s interests, expertise and the availability of relevant data.

The ideal candidate will have a strong interest in mental health and experience in quantitative research. Applicants may come from psychology, mental health, epidemiology, public health, behavioural science, statistics, data science, health sciences, computer science or a related discipline. Experience with R, Python, Stata or comparable analytical software would be advantageous. Candidates are not expected to possess advanced expertise in every area but should be willing to develop interdisciplinary skills in mental health research, behavioural data science and large-scale longitudinal data analysis.

NAMES OF SUPERVISORS: Dr Tianhua Chen (University of Huddersfield), Professor David Peebles (University of Huddersfield)

Applications

To apply please submit the documents listed below to pgrscholarships@hud.ac.uk in full by Friday 13 November 2026.

• A motivational email as to why you wish to apply for the scholarship, stating which project you are applying for.
• A 2,000-word research proposal outlining how you plan to undertake for the research topic you are applying for. Find further guidance on writing a research proposal.
• A copy of your CV.
• Copies of transcripts and certificates of all relevant academic and/or any professional qualifications.
• References from two individuals (at least one to be an academic reference). Please contact your referees and ask them to send your references directly to pgrscholarships@hud.ac.uk from their work email address (references can be submitted late but must be received by Friday 4 December 2026).
• Proof of eligibility – e.g. a scan of your passport photo page.

Please state the research project title in the subject area of the email when sending through your documents.

Need further support?

For any queries regarding eligibility or submitting documents, please email: pgrscholarships@hud.ac.uk

For subject specific enquiries please contact Dr Tianhua Chen t.chen@hud.ac.uk

Details of the project can be found by visiting https://www.hud.ac.uk/postgraduate/research/research-scholarships/cemh-fully-funded-scholarships/