Postdoctoral Research Assistant in Multimodal Analysis to understand Medical Procedures

Location: Old Road Campus, Headington, OX3 7DQ

We are seeking a creative and highly motivated postdoctoral researcher to join the Turing AI World-Leading Fellowship research programme lead by Professor Alison Noble. Topics in the research programme include single- and multi-modal video-based human-AI collaboration, federated learning for healthcare collaboration and partnership, and applied research to understand human skill in healthcare settings and AI to support healthcare professional skills training. The appointee will be a technical researcher joining the AI to understand human skills strand of the research programme. This post is focused on bridging theory and application of medical video/multimodal analysis and medical procedure modelling. The research will involve building video/multimodal models that can analyse medical procedures and evaluate how usable they are. Evaluation may involve quantitative studies (model performance) and qualitative studies (human factors assessment).

The appointee will be part of the Noble research group at the Institute of Biomedical Engineering, based at the Old Road Campus in Headington. The post is funded by UKRI and is fixed-term for 18 months.

You will be working in a small, highly motivated interdisciplinary team working towards a shared goal. You will be responsible for the design and pilot testing of machine learning-based automated medical video/multimodal analysis models. The research will include work that aims to understand medical procedures using video/multimodal analysis. This requires solid technical experience in medical video/multimodal analysis and building efficient learning approaches from sparse data, as well as open-mindedness to co-develop blue-sky research questions with clinical collaborators from first principles. The work will involve both computational methods research and collaboration with experts in clinical medicine and human factors to design and evaluate models in simulated and real clinical scenarios. Evaluation may involve quantitative studies (model performance) and qualitative studies (human factors assessment).

You should hold a relevant PhD/DPhil (or near completion\*) and publications in medical image/video analysis, computer-assisted intervention, or video analysis in computer vision. Knowledge of a specific medical procedure is not a requirement but a genuine interest in research at the interface of machine learning with clinical demand and the curiosity to develop blue-sky research questions is.

Informal enquiries may be addressed to Dr Cheng Ouyang (email: cheng.ouyang@eng.ox.ac.uk)

For more information about working at the Department, see

www.eng.ox.ac.uk/about/work-with-us/

Only online applications received before midday on the 2nd November 2026 can be considered. You will be required to upload a covering letter/supporting statement, including a brief statement of research interests (describing how past experience and future plans fit with the advertised position), CV and the details of two referees as part of your online application (via the ‘Apply’ button above).

The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology.

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Medical procedure analysis, multimodal learning, video analysis, human-AI collaboration, healthcare AI