Postdoctoral Research Associate (PDRA) in Machine Learning Methods for Mental Health
Role details
Job location
Tech stack
Job description
The PDRA will be an independent researcher working with a significant degree of autonomy within LUMHR's Connect theme, hosted in the School of Engineering & Physical Sciences. The role focuses on developing and applying machine learning, statistical, spatial and temporal modelling approaches to understand mental health need, crisis trajectories, service entry patterns, and system performance across rural, coastal, and small urban-deprived settings.
You will design, implement and validate analytical models using large-scale, linked health and socio-environmental datasets, working closely with academic colleagues, NHS and Integrated Care System analytics teams, local authorities, and community partners. The role also involves contributing to data pipelines, visualisation tools, and reproducible analytical workflows, and producing high-quality research outputs suitable for both methods-led and applied journals.
You will collaborate across LUMHR themes (particularly with colleagues working on crisis care and prevention), support interdisciplinary research activity, and contribute to grant development aligned with your research interests. Teaching support may be required, up to a maximum of six hours per week.
Requirements
Do you have a Doctoral degree?, You will have a PhD (or near completion) in a relevant discipline (e.g., data science, computer science, engineering, statistics, or a related field) or equivalent research experience. You will have demonstrable expertise in machine learning and/or advanced analytical methods, experience working with complex or large-scale datasets, and strong programming skills (e.g., Python or R).
You will be able to communicate complex analytical findings to non-technical audiences and will have a strong commitment to ethical, responsible, and impactful research. Experience applying analytical methods in applied, interdisciplinary, or health-related contexts is particularly welcome.
Benefits & conditions
This post is permanent and full-time (1.0 FTE) and offers the opportunity to develop an independent, methods-led research career at the intersection of advanced analytics and applied mental health research, within a highly collaborative and multidisciplinary environment.
