Research Associate in Data Science

Applicants are invited for the above vacancy in the Division of Informatics, Imaging and Data Sciences, University of Manchester.

You will join the Division and take responsibility for an area of research under the supervision of Professor Evan Kontopantelis.

The BHF CRE is an £8 million initiative (50% funded by the BHF, 50% by the University) to transform cardiovascular research and care through interdisciplinary science, innovation, and equity. It builds on Manchester’s strengths in genomics, data science, inflammation, and translational medicine, and is embedded within a vibrant health innovation ecosystem.

**The CRE aims to**:

- Deliver world-leading cardiovascular research from molecules to populations.
- Address health inequalities in cardiovascular outcomes.
- Train the next generation of interdisciplinary cardiovascular researchers.
- Translate discoveries into real-world impact through NHS and industry partnerships.

There are five research themes within the BHF CRE
- ** Cardiovascular Genomics and Development**
- ** Focus: Genetic and developmental mechanisms of cardiovascular disease.**:

- Goals: Discover causal variants, understand congenital heart disease, and advance pharmacogenomics.
- ** Heart Failure**
- ** Focus: Mechanisms and treatment of heart failure, especially HFpEF.**:

- Goals: Identify therapeutic targets, develop biomarkers, and evaluate novel therapies.
- ** Inflammatory Drivers of Cardio
- and Cerebrovascular Disease (IDCCD)**
- ** Focus: How inflammation contributes to cardiovascular and cerebrovascular disease.**:

- Goals: Identify mechanisms, develop diagnostics, and test anti-inflammatory therapies.
- ** Cardiovascular Data Science**
- ** Focus: Using big data to improve cardiovascular prediction, care, and equity.**:

- Goals:

- Develop advanced risk prediction models using EHRs, imaging, and environmental data.
- Quantify health inequalities and model interventions.
- Support all other themes with analytical infrastructure.
- ** Computational Modelling, Simulation and Large Language Models (CMSL)**
- ** Focus: In silico trials, AI, and digital twins for cardiovascular science.**:

- Goals: Build predictive models, simulate device performance, and develop CardioLLM.

The post holder would be working in Theme 4 'Cardiovascular Data Science', under the supervision of theme lead, Professor Evangelos (Evan) Kontopantelis.

**The Project**

The Post Holder will join the British Heart Foundation Centre of Research Excellence (BHF CRE) at The University of Manchester, contributing to Theme 4: Cardiovascular Data Science. This theme aims to harness large-scale, multimodal data to transform cardiovascular care, with a strong emphasis on equity, innovation, and interdisciplinary collaboration.

Theme 4 leverages Manchester’s unique access to large-scale, linked electronic health records (EHRs), imaging data, genomic resources, and environmental datasets. These include the Greater Manchester Secure Data Environment (GM SDE), national cardiovascular audit datasets (e.g. MINAP, BCIS, TAVI, NHFA), and the UK Biobank, which is relocating its headquarters to the University of Manchester campus.

**The post holder will play a key role in delivering the following objectives**:

- Develop and validate advanced cardiovascular risk prediction models, including multi-outcome and dynamic models tailored to complex, multimorbid populations. These models will support personalised care and shared decision-making in clinical practice.
- Leverage multimodal data sources, including EHRs, imaging, genomics, and environmental exposures, to support predictive modelling, deep phenotyping, and real-world evidence generation.
- Quantify and address cardiovascular health inequalities, by analysing disparities in care and outcomes across geography, ethnicity, socioeconomic status, and comorbidity. This includes spatial epidemiology and modelling of environmental determinants of cardiovascular disease.
- Support the analytical infrastructure of the CRE, enabling cross-theme collaboration and integration of data science into discovery, translational, and clinical research.
- Contribute to the development of a cardiovascular-specific large language model (CardioLLM) in collaboration with Theme 5 (Computational Modelling, Simulation and Large Language Models), to support clinical decision-making and knowledge discovery.
- Engage with national and international collaborators, including the BHF Data Science Centre, NHS partners, and academic institutions, to ensure the scalability and impact of research outputs.
- Mentor and support early-career researchers and trainees, contributing to the CRE’s commitment to capacity building and interdisciplinary training in cardiovascular data science.

This is an exciting opportunity to contribute to a nationally significant programme of work that will shape the future of cardiovascular research and care. The post holder will be embedded in a vibrant, collaborative env