AI Research Officer - DATAMIND TRE & Observatory MHG

About The Role

This is a Fixed Term position until May 2028 working full-time.

We are seeking a Machine Learning (ML) Researcher to advance AI capabilities within DATAMIND TRE and the Mental Health Goals (MHG) Observatory (national infrastructure supporting the UK Government's commitment to mental health research). This is a 24-month fixed-term position (to March 2028) with strong extension potential, as the MHG programme is funded until March 2030.

While ML holds transformative potential for mental health research, applying these methods within Secure Data Environments (SDEs) requires specialized infrastructure and practices that align with epidemiological rigor. You will bridge this gap by making large-scale health datasets ML-ready, developing tools for responsible ML research within SDEs, and demonstrating best practices through exemplar projects.

Working within our SDE, you will:

• Engineer data pipelines that transform billions of health records into ML-ready formats while preserving data provenance and epidemiological validity.
• Develop and deploy ML tools, and workflows designed for the constraints and requirements of SDEs.
• Lead applied research projects that showcase how modern ML methods can advance mental health and suicide prevention research.

You will join a world-leading team led by Prof. Ann John (director of DATAMIND, SAGE member, Samaritans Trustee) managing over £20M in mental health research funding. Based at Swansea University Medical School (ranked #1 in the UK for Research Environment, REF 2021), you'll work with unique population-scale and cohort datasets. Enjoy flexible hybrid working minutes from the Gower Peninsula's stunning coastline, with competitive benefits including 31 days annual leave, 14.5% employer pension contribution (USS), and a lower cost of living than London or the Southeast.

We are seeking an ambitious researcher or data scientist with strong ML foundations, programming expertise (Python/SQL), and interested in bridging ML methods with epidemiological research. You combine technical rigor with the ability to communicate complex methods to clinical and public health researchers.