Website BristolUni University of Bristol
Closing date: 1st December
The University of Bristol seeks a PhD student to develop explainable AI methods that combine health records, imaging, wearables and other digital health data to detect conditions such as dementia and Parkinson’s earlier, on a competition-funded PhD with Dr Qiang Liu.
About the Project
Many chronic and progressive conditions, including dementia, Parkinson’s disease, cardiovascular disease, diabetes and mental health disorders, develop silently for years before they are diagnosed. Current diagnostic pathways typically rely on clinical assessments, laboratory tests and imaging carried out only once symptoms have become apparent, by which point opportunities for prevention and early intervention are limited. Meanwhile, the growth of digital health is generating unprecedented volumes of data, from electronic health records and medical imaging to wearable sensors, smartphones, speech and everyday behavioural signals. This PhD project will develop novel artificial intelligence methods that turn these rich, heterogeneous data sources into earlier, more accurate and more personalised insights into health and disease.
The project will investigate how clinical, imaging, physiological, behavioural, genetic and free-text data can be integrated to model disease onset and progression over time. Depending on the student’s interests, research directions may include:
- multimodal deep learning and data fusion
- foundation models and large language models for health records and clinical text
- time-series analysis of data from wearable and remote monitoring devices
- methods that remain robust when data are incomplete, noisy or collected across different populations and settings
The project will develop explainable, fair and uncertainty-aware models so that clinicians and patients can understand and scrutinise their predictions. Models will be evaluated not only for predictive accuracy but also for their practical value, including how they could support screening, remote monitoring and clinical decision-making within real healthcare workflows.
The student will develop expertise spanning machine learning, health data science and digital health.
To apply for this job please visit www.findaphd.com.
