Artificial Intelligence for cardiovascular imaging and clinical big data
Monday 29th September 2025
Contact Email for the Job Positing qiang.zhang@cardiov.ox.ac.uk
Organization University of Oxford (Divison of Cardiovascular Medicine)
Location Oxford, UK
Title Artificial Intelligence for cardiovascular imaging and clinical big data
URL https://www.rdm.ox.ac.uk/graduate-study/how-to-apply/DPhil-Research-Projects/2026-zhang-group-artificial-intelligence-for-cardiovascular-imaging-and-clinical-big-data
Closing date Dec 02, 2025
Description The project's primary aim is to advance cardiac diagnostic imaging and enrich cardiovascular clinical studies through the deep integration of AI machine learning with cardiovascular medicine, imaging and cardiac big data. Students will explore various opportunities addressing unmet clinical needs in cardiac imaging using novel machine learning approaches. This includes:
(1) Automating cardiovascular MRI analysis and reporting, at scale or in real time, using pipelines powered by machine-learning models.
(2) Making cardiovascular MRI scanning safer, faster and more informative by enhancing the image contrast with novel generative AI approaches. A representative example is the Virtual Native Enhancement technology.
(3) Enriching large-scale biomedical, clinical and population studies with novel AI-derived imaging biomarkers and machine-learning tools, in collaboration with the Big Data Institute.
Students will be able to explore and develop a DPhil project focused on one of these three themes, tailored to their expertise and interests. They will have access to large clinical databanks to support their deep learning developments.
Training opportunities:
The students will be jointly supervised by AI experts, MRI scientists and cardiologists specialised in cardiovascular imaging, and develop novel methodologies at the forefront of cardiac diagnostic imaging, at the intersection of deep learning, big data and cardiovascular healthcare. Additional computing and networking resources can be supported by the Big Data Institute.
Applicants are expected to have a strong expertise in deep learning and machine learning. Additional experience in medical imaging and healthcare statistics is advantageous.
Students are encouraged to attend the MRC Weatherall Institute of Molecular Medicine DPhil Course, which takes place in the autumn of their first year. Running over several days, this course helps students to develop basic research and presentation skills, as well as introducing them to a wide range of scientific techniques and principles, ensuring that students have the opportunity to build a broad-based understanding of differing research methodologies.
Generic skills training is offered through the Medical Sciences Division's Skills Training Programme. This programme offers a comprehensive range of courses covering many important areas of researcher development: knowledge and intellectual abilities, personal effectiveness, research governance and organisation, and engagement, influence, and impact. Students are actively encouraged to take advantage of the training opportunities available to them.
As well as the specific training detailed above, students will have access to a wide range of seminars and training opportunities through the many research institutes and centres based in Oxford.
The Department has a successful mentoring scheme, open to graduate students, which provides an additional possible channel for personal and professional development outside the regular supervisory framework. We hold an Athena SWAN Silver Award in recognition of our efforts to build a happy and rewarding environment where all staff and students are supported to achieve their full potential.
Additional Supervisors
1. Stefan Piechnik
2. Vanessa Ferreira
Publications
1 https://doi.org/10.1016/j.jocmr.2024.101051
2 https://doi.org/10.1016/j.media.2021.102029
3 Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping - PubMed
4 https://doi.org/10.1161/CIRCULATIONAHA.121.054432
5 https://doi.org/10.1161/CIRCULATIONAHA.122.06013