University of Lincoln- School of Engineering & Physical SciencesLincoln

Postdoctoral Research Associate (PDRA) in Machine Learning Methods for Mental Health

Description

Are you passionate about using data science and machine learning to address mental health inequalities in rural and coastal communities?

The University of Lincoln is seeking an ambitious Postdoctoral Research Associate (PDRA) with strong machine learning and data science expertise to join the Lincolnshire Unit for Mental Health Research (LUMHR)

  • a major NIHR-funded initiative focused on improving mental health and wellbeing in rural, coastal, and underserved communities across Lincolnshire.

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.

About the role

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.

About you

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.

About us

LUMHR is Lincolnshire’s first integrated, multidisciplinary unit dedicated to applied mental health research in rural, coastal, and small urban-deprived settings. Funded through the NIHR Mental Health Research Group programme, LUMHR brings together academic, clinical, community and lived-experience partners to address persistent mental health inequalities.

The University of Lincoln is proud to be a recipient of the Queen’s Anniversary Prize for Higher Education (2023) and is based in the heart of one of the UK’s great historic cities.

Informal enquiries

For informal enquiries or further information, please contact: XXXX XXXX

Apply Online

Further details:

We strive for a diverse workforce with the very best employees and are committed to creating an inclusive environment for all. The University encourages applications from underrepresented groups inclusive of Black, Asian and other minoritised/marginalised ethnic groups, all gender identities and expressions from the LGBTQIA+ community, candidates with a disability, and those that practise different faiths and beliefs, to enhance our One Community where we strive to be kind, patient, and supportive of each other.

Skills

PythonMachine Learning

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