Description
We are excited to announce an opportunity for an Imaging AI Postdoctoral Research Associate position in the group of Marc Dewey (Professor of Radiology) at the University of Cambridge.
This position is funded by the first Academy of Medical Sciences (AMS) Professorship grant to the University of Cambridge (https://acmedsci.ac.uk/more/news/academy-international-research-professorships).
The position will play a central role in the coronary imaging AI and histology project of the AMS Professorship grant aiming to achieve the first histology-informed, near-microscopic resolution AI-based CT imaging of coronary atherosclerosis. This is a unique opportunity to help pioneer the next generation of cardiovascular imaging by combining photon-counting CT, micro-CT, 3D histopathology, and advanced AI.
During this project you will help building the first large-scale multimodal coronary CT-histology database, develop novel super-resolution algorithms, and contribute to mechanistic insights into coronary atherosclerosis development. The successful candidate will join a multidisciplinary team (www.marcdewey.de and Instagram: @dewey_team) spanning engineering, imaging, cardiovascular medicine, and computer sciences working closely with leading global partners.
You will also contribute to internationally significant randomised imaging trials led by our group, including CAD-Man and DISCHARGE, which have resulted in high-impact publications (e.g., BMJ and New England Journal of Medicine) and thereby shaped international guidelines and advanced research in cardiovascular and cardiothoracic imaging. You will also support ongoing and new multicentre imaging trials (such as INCHARGE) focused on improving the diagnosis, monitoring, and treatment of cardiovascular and cardiothoracic diseases.
We welcome applications from technically exceptional candidates with a PhD (or equivalent) in a relevant subject area (e.g., Medicine, Computer Science, Engineering, Mathematics).
Expertise in image data handling, multimodal registration, deep learning, strong foundation in Python, R, and SQL, and quantitative imaging are key.
A strong developing publication record and evidence of independent problem-solving are essential.
Candidates who have submitted their PhD thesis but are awaiting award are also encouraged to apply. They will have some research experience with a developing track record of publications, and a strong foundation in software development and data analysis and ideally image analysis software and quality assurance. They will also bring excellent presentation and teaching skills and be willing to take a leading role in supervising research students.
The purpose of the Role.
- Leading the development of next-generation, histology-validated coronary imaging AI methods as part of a major AMS Professorship project.
- Integrating clinical coronary CT including photon-counting CT, micro-CT, and 3D histology data into a unified research pipeline.
- Advancing AI-based super-resolution and feature extraction methods to achieve near-microscopic coronary plaque characterisation.
Key Responsibilities:
- Developing and optimising multimodal imaging databases and AI pipelines for photon-counting CT, micro-CT, and histology.
- Performing high-precision rigid and deformable registration using tools such as Elastix or TransMorph.
- Designing and training deep-learning models (e.g., GANs, ResNets, ViTs) for super-resolution and plaque classification.
- Curating and analysing large-scale imaging datasets, including ex-vivo cardiac specimens.
- Collaborating with international teams and supporting integration with synchrotron-imaging.
- Supporting ongoing and new multicentre imaging trials (such as INCHARGE).
- Supporting international expert meetings and colloquiums (such as the 4th QCI meeting on 18 September 2026).
- Preparing high-impact publications, contributing to grant writing, and supporting student supervision.
- Presenting research findings at international meetings
Informal enquiries or questions relating to the application process may be directed to ClusterHR@medschl.cam.ac.uk.
Apply online: https://www.cam.ac.uk/jobs/imaging-ai-research-associate-fixed-term-rq48442
