Research Fellow at The University of Edinburgh

Part-time – 21 hours per week

Fixed term until October 2025

The Edinburgh Cancer Centre seeks to appoint a post-doctoral researcher with experience of machine learning, image analysis and radiotherapy to the PROSECCA project, a 3 year project that brings together a large team comprising, 2 Principal Investigators, 24 Co-Investigators, 12 collaborators and a patient representative. The aim of this project is to use advanced machine learning and artificial intelligence techniques to analyse healthcare records from thousands of prostate cancer patients who underwent radiotherapy in the treatment of their cancer. From this, new relationships between a patient’s medical history, and how well they respond to radiotherapy in treating their cancer, will be established and help identify patients at an increased risk of toxicity. This will also improve radiotherapy treatment for prostate cancer patients in the future.

The post is part-time, fixed term for 2 years and the salary is on the University Grade 7.

The opportunity:

The successful candidate will have a track record and experience in machine learning and radiotherapy or a related discipline. You will be an effective communicator with an ability to explain complex technical concepts to a multi-disciplinary team. You will have demonstrated your skills in writing and presenting your work in high-quality publications and working to project deadlines. You will have good knowledge of developing technical solutions for healthcare problems and an awareness of large-scale data handling.

For informal discussion, potential candidates are invited to contact the Prof Bill Nailon at +44 131 537 3560 or W.Nailon@ed.ac.uk or Bill.Nailon@nhslothian.scot.nhs.uk 

Your skills and attributes for success: 

• Knowledge of machine learning, computer vision and image analysis
• Experience of interdisciplinary, healthcare-oriented projects
• Previous experience or working knowledge of radiotherapy 
• The ability to extract data and a good understanding of transferring and transforming data, for analysis using different statistical packages
• Ability to organise their own work and to work effectively as a member of a team