PhD for 2026 entry: Deep learning of multimorbidity trajectories in cardiovascular disease: capturing cross-talk between diseases with AI and big data

Saturday 27th September 2025

Contact Email for the Job Positing qiang.zhang@cardiov.ox.ac.uk
Organization University of Oxford (Big Data Institute)
Location Oxford, UK
Title PhD for 2026 entry: Deep learning of multimorbidity trajectories in cardiovascular disease: capturing cross-talk between diseases with AI and big data
URL https://www.ndph.ox.ac.uk/study-with-us/dphil-population-health/choose-a-dphil-project-2026/deep-learning-of-multimorbidity-trajectories-in-cardiovascular-disease-capturing-cross-talk-between-diseases-with-ai-and-big-data
Closing date Dec 02, 2025
Description Background
Cardiovascular disease remains the leading cause of death globally. Emerging evidence increasingly reveals the complex cross-talk between cardiac pathology and dysfunctions in other organs, highlighting diverse, overlapping multimorbidity trajectories. This growing complexity in disease progression and risk prediction calls for novel, data-driven approaches to aid the understanding of disease mechanism.
Advances in artificial intelligence (AI) and the availability of large-scale population data, such the UK Biobank imaging cohort, provide powerful lens and materials to uncover the systemic nature of cardiovascular disease, but also capture the individual disease trajectories for precise and personalised interventions.
This project aims to develop novel AI machine learning approach on UK Biobank imaging cohort, to:
Capture image evidence of multi-organ associations in cardiovascular disease using machine learning for medical imaging;
Study the associations of myocardial injuries identified by novel AI-enhanced imaging, such as virtual native enhancement of cardiac MRI, with conditions of multiple organs using advanced machine learning models.
Develop personalised risk prediction and recommendation models based on the new findings.
research experience, research methods and skills training
You will benefit from an interdisciplinary core supervision team of machine learning scientists, biomedical statisticians and cardiologists, and develop and validate deep generative AI models at population scale. Skills training includes machine learning (deep learning), cardiac imaging and data analysis for large population studies.
The project is in partnership with Oxford Centre for Clinical Magnetic Resonance Research (OCMR) which supports expertise in cardiac imaging and cardiovascular medicine.
Research works are expected to lead to several novel publications at the intersection of AI and healthcare.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
You will be primarily based at Big Data Institute. Additionally, you will have access to facilities, mentorship and networking at RDM Division of Cardiovascular Medicine. You will contribute to the collaborations between the two departments and the integration of large-scale cardiovascular image analysis with machine learning methods.
PROSPECTIVE STUDENT
The ideal candidate will have a strong background in computer science, machine learning, and/or healthcare statistics, and have research experience in medical imaging.