Research Associate in Radiology

University of Cambridge Department of Radiology

United Kingdom

Two postdoctoral research positions are available working on a collaborative project to develop an AI-based diagnostic and prognostic tool to support COVID-19 patient management in the hospital. Post 1 is 0.6 FTE (22.2 hours per week) while Post 2 is full-time. These roles are part of a multi-disciplinary project led by Professor Carola-Bibiane Schönlieb, at the Department of Applied Mathematics and Theoretical Physics, and Professor Evis Sala, at the Department of Radiology, University of Cambridge. Both positions will be based within the Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Cambridge.

COVID-19 is a disease that significantly impacts the world's health and economic welfare. Effective healthcare delivery is challenged by limited knowledge of COVID-19 and a lack of experience managing it. However, large amounts of relevant data are available, and, if appropriately assimilated and analysed, could improve outcomes in patients with COVID-19. Diagnosis, triage, treatment response assessment and prognosticating the COVID-19 patient accurately is crucial. We believe artificial intelligence (AI), in the form of machine learning, paired with rigorous mathematical and statistical techniques can help. By processing and analysing complex, multi-stream patient data, AI can identify different clinical phenotypes of the disease, predict disease progression, and optimise the management of an individual patient.

The project will develop a robust, generalisable and clinically-informed open-source AI tool called AIX-COVNET, integrating chest imaging, clinical and laboratory data to support the diagnosis, triaging, treatment planning and monitoring of hospital patients with COVID-19. We envision AIX-COVNET to be a tool that can optimise COVID-19 management in hospitals. Having optimal treatment will no doubt save lives and allow conservation and better deployment of NHS resources.

Further information is available on the project website:

Duties include developing collaborative research objectives and defining individual project work streams. The role holder will be expected to plan and manage their own research and administration, with guidance if required, and to assist in the preparation of proposals and applications to external bodies. You must be able to communicate material of a technical nature and be able to build internal and external contacts. You may be asked to assist in the supervision of student projects, the development of student research skills, provide instruction and plan/deliver seminars and presentations relating to the research area.

Candidates will have a PhD in mathematics or statistics (or a related discipline) coupled with a research interest in data analysis. They will have experience in one or more of the following: inverse problems, machine learning, neural networks, and/or (medical) image analysis. In particular, knowledge of integrating multi-stream medical data in support of clinical decision-making processes, and experience with longitudinal image analysis or RNNs would be advantageous.

Fixed-term: These posts each have funding for up to three years in the first instance.

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

Informal enquiries about the position may be made to the coordinator for this recruitment at:

Please indicate the contact details of two academic referees on the online application form and upload a cover letter, full curriculum vitae and a one-page summary of research achievements and interests in relation to the project. Please indicate if you are interested in the part-time post (post 1) or full-time post (post 2) or both posts on your cover letter. Please ensure that both of your referees are contactable at any time during the selection process, and are made aware that they will be contacted by the Mathematics HR Office Administrator to request that they upload a reference for you to our Web Recruitment System; and please encourage them to do so promptly.

Please quote reference LE23204 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society. We particularly welcome applications from women and /or candidates from a BME background for this vacancy as they are currently under-represented at this level in our Department.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

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