Postdoctoral Researcher in Computerised Tomography Image Analysis

University of Oxford

Contract type: Fixed term for 36 months

Hours: Full-time

You will be based in Nuffield Department of Surgical Sciences, John Radcliffe Hospital  as your normal place of work; but you may be able to agree a pattern of regular remote working with your line manager.

About the role

We are seeking a Full-Time Postdoctoral Researcher in Computer Vision CT image analysis to join the Nuffield Department of Surgical Sciences (John Radcliffe Hospital, Headington, Oxford), working in close collaboration with the Biomedical Image Analysis group. (Biomedical Institute of Biomedical Engineering, Old Road Campus Building, Headington, Oxford). The post is funded by UK Research Innovation for the duration of up to 36 months.

This is a unique opportunity to develop your career in the intersection of: Healthcare, Academia and Industry. Your research will translate to real world impact that improves patient journeys, healthcare system efficiency and environmental sustainability.

In addition to further excelling your skills in Computer Vision/Big Data/Machine Learning analyses, this opportunity enables you to:

v  Work closely with clinicians and academic researchers in multiple countries across different continents.

v  Interact with industry collaborators in different sectors of healthcare AI and gain interdisciplinary insights.

v  Develop leadership / management skills through:

  • wide range of training courses available through University of Oxford and UK Research Innovation ecosystems.
  • ‘on-the-job’ training through your pivotal roles in the international consortium ( In addition to developing the scientific research, you will have the opportunity to gain management / leadership skills in aspects of the consortium operations.

v  Enriched support for research grant applications, including research fellowships

v  Lead research projects through its full life cycle, including IP capture and downstream exploitation.

Your scientific research will focus on the development of machine/deep learning-based medical image analysis methods for computerised tomography scans (CT scans). Key applications of such ML/DL methods are illustrated by our prior research (PMIDs 33913675, 33234786, 33630463, 35286501) where we showed the potential for such applications to be ‘platform technologies’. We will next iteratively refine the clinical applications in different anatomical regions and in different pathological contexts.

You will have access to an unprecedented CT image repository (in terms of its volume and diversity) managed by the AICT consortium, as well as datasets acquired through open access platforms and image providers. In addition to refining the existing DL architecture, we will explore the utility of Foundation Models (diffusion models, active learning) and other emerging DL paradigms to this foundation data source.

About you

You should possess a relevant PhD/DPhil (or be near completion), as well as relevant experience in the area.  You should also have previous experience of contributing to publications/presentations and the ability and enthusiasm to deliver results in interdisciplinary research.  Excellent communication skills, including the ability to write for publication, present research proposals and results, and represent the research group at meetings are also essential.  Experience in the analysis of CT images, particularly in the context of translational projects would be beneficial.

Application Process

This full-time post and is fixed-term for 36 months in the first instance. Informal enquiries may be addressed to Prof Regent Lee, Principal Investigator: UKRI Future Leaders Fellow ( or Prof Vicente Grau ([email protected]).

Applications for this vacancy are to be made online. You will be required to upload a supporting statement of research, setting out how you meet the selection criteria, curriculum vitae and the names and contact details of two referees as part of your online application. Please quote reference NDSA833 on all correspondence.

Only applications received before noon on Friday 30 June 2023 can be considered.

Interviews will be held on Friday 14 July 2023.

Committed to equality and valuing diversity

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