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Website BristolUni University of Bristol
Closing date: 31st March
University of Bristol offers funded PhD to develop interpretable AI models predicting Alzheimer risk from sleep, genetics and clinical data.
Shortlisted candidates will be invited to discussions with the supervisory team and representatives of the funder in mid- to late March 2026, with a planned start date of September 2026.
Project Overview and Research Focus
Alzheimer’s disease (AD) is the leading cause of dementia and remains a major public health challenge. Reliable early prediction of AD risk before irreversible neurodegeneration occurs could transform prevention and early intervention strategies.
This PhD will develop interpretable artificial intelligence (AI) models to predict future risk of clinically diagnosed Alzheimer’s disease using multimodal data, e.g., wearable sleep time-series data, polygenic risk scores (PRS), APOE ε4 genotype, and clinical risk factors. Genetic inputs will focus on PRS and established AD risk markers to ensure feasibility and biological relevance within a PhD timeframe. WGS would be exploratory only and contingent on time and data readiness. The student will build predictive models integrating sleep profiles, genetic risk, and clinical variables using approaches such as time-to-event modelling and deep learning architectures. Explainable AI methods will be applied to identify interpretable sleep–genetic–risk interactions and ensure clinical usability.
In addition to model development, the project includes clinical validation within the University of Bristol Brain Health Clinics, working closely with clinicians to evaluate model interpretability, relevance, and potential integration into real-world decision-making pathways.
The project is embedded within a vibrant research environment linking engineering, neuroscience, psychiatry, and NHS dementia services, with access to world-leading datasets and strong clinical translation pathways.
Candidate Profile
We welcome applicants from quantitative, computational, biomedical, or clinical backgrounds who are motivated by translational research.
You may have:
A background in AI, machine learning, statistics, mathematics, computer science, engineering or related disciplines, with experience analysing health or genetic data and an interest in clinically meaningful applications;
or
A background in genetics, neuroscience, psychology, psychiatry, medicine or related biomedical sciences, with strong analytical skills and a clear interest in developing expertise in statistical modelling, deep learning, and AI.
This is not a clinical training post. However, the project involves close collaboration with clinicians and patient-facing research teams. We therefore seek a candidate who is enthusiastic about clinically translational, interdisciplinary research.
Applicants should hold (or expect to obtain) a first-class or strong upper second-class degree and ideally a Master’s degree (or equivalent experience) in a relevant field. Programming experience (e.g. Python or R) and strong quantitative skills are essential.
Funding
This is a fully funded 4-year PhD studentship jointly funded by BRACE Dementia Research and the University of Bristol. The award covers tuition fees at the UK Home rate, a tax-free stipend at UKRI rates, and research and training costs. International applicants are welcome to apply, provided they are able to self-fund the difference between Home and Overseas tuition fees. Documented evidence of the ability to self-fund this difference need to be provided.
Supervisory Team and Environment
The student will be supervised by Dr Qiang Liu (AI & Digital Health), Prof. Elizabeth Coulthard (Cognitive Neurology), Dr Jonathan Blackman (Older Adult Psychiatry & Sleep), Dr Hanna Isotalus (Digital Health & Sleep Neuroscience), and Dr Ivan Koychev (Neuropsychiatry, Imperial College London).
For enquiries, please contact:
Dr Qiang Liu
To apply for this job please visit www.findaphd.com.
