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.

• 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.
• Apply and refine causal inference methodologies, such as structural equation modelling and Bayesian approaches, to better understand the effectiveness of interventions in populations often excluded from clinical trials (e.g. patients with cancer, ethnic minorities, and those with multiple long-term conditions).
• 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 environment with access to cutting-edge infrastructure, mentorship, and opportunities for career development.