4 X Postdoc Positions in Artificial Intelligence/machine Learning for Medical Imaging

We are seeking to appoint four Research Associates to develop scalable and robust artificial intelligence and machine learning techniques for the analysis and interpretation of medical images and related clinical data. The development of the artificial intelligence (AI) and machine learning (ML) will enable faster and early diagnosis, automation of expensive and time-consuming manual reporting, enhanced patient screening and stratification for optimal treatment. Ultimately, this will support a major redesign of clinical pathways to improve outcomes and efficiency.

The successful candidates will work as part of the newly-established London Medical Imaging & Artificial Intelligence Centre, a new network funded by Innovate UK and the EPSRC. The London Medical Imaging & Artificial Intelligence Centre has been established to improve NHS patient care and health outcomes, reduce healthcare costs and support the growth of companies, supporting the economy. Its aim is to apply artificial intelligence technologies to medical imaging (for example, MRI scans, CT scans, PET scans and ultrasound). The Centre is a collaboration between three excellent universities (King's, Imperial and QMUL), four leading NHS trusts as well as companies (Siemens, NVIDIA, IBM, GSK) as well as a number of UK-based SMEs in the area healthcare-based AI.

You will be part of the Biomedical Image Analysis Group based at the South Kensington campus. The mission of the group is to develop novel, computational techniques for the analysis of biomedical images. For further information on the group see: http://biomedic.doc.ic.ac.uk/. The Group is part of the Department of Computing at Imperial College London which is a leading department of Computer Science in the UK.

*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range 33,380 – 35,061 per annum.

Should you have any queries regarding the application process please contact Jamie Perrins : j.perrins@imperial.ac.uk