Image Analysis and AI Scientist in Oncological Imaging

University of Sheffield - Faculty of Health

Location: Sheffield

Salary: £38,784 to £47,389 per annum. Potential to progress to £51,753 per annum through sustained exceptional contribution.

Hours: Full Time

Contract Type: Fixed-Term/Contract

Placed On: 8th April 2026

Closes: 29th April 2026

Job Ref: 2399

Are you interested in working for a world top 100 University? We have an exciting opportunity available for an experienced image analysis and computing scientist with experience in both classical image processing and artificial intelligence to engage in cutting-edge, multi-modal cancer imaging research as part of the Sheffield Platform for Imaging Research in Oncology (SPIRO) at the University of Sheffield.

You will be based within the SPIRO initiative in the Division of Clinical Medicine under the mentorship of Dr Bilal Tahir, Professor Jim Wild and the SPIRO co-investigators. SPIRO represents a £4M+ transformative capital investment funded by Yorkshire Cancer Research to establish Sheffield as a leading centre for oncological imaging research, leveraging Sheffield’s unique imaging infrastructure including Yorkshire’s only MRI-PET scanner and the North of England’s only Photon-Counting CT scanner.

This role will involve developing and applying advanced image analysis techniques, including both classical and AI-based methods, to extract meaningful insights from multi-modal cancer imaging data across PET, MRI, and Photon-Counting CT. You will develop strategies to analyse large datasets of 3D images to aid in numerous image analysis tasks, including cancer phenotyping, classification, and prediction of treatment response, working closely with the Insigneo Institute for in silico Medicine and the Centre for Machine Intelligence.

We are seeking a candidate with a good Honours degree in Physics, Engineering, Computer Science or related discipline and a PhD in medical image processing/analysis (or equivalent). Experience in machine learning/deep learning as well as classical image processing techniques is essential. A proven track record of high-quality scientific publications and demonstrated evidence of working in a multi-disciplinary team are essential.

If you are passionate about methodology-driven translational cancer imaging research, then we would love to hear from you.