The University of Cambridge Department of Radiology, under the leadership of Professor Fiona Gilbert, has a well-established track record in breast cancer imaging. As artificial intelligence (AI) becomes increasingly prominent in this field, its adoption into clinical practice depends on rigorous evidence from both retrospective and prospective studies.
We are seeking a highly motivated individual to join our dynamic team as a key contributor to a growing portfolio of research in breast cancer screening. This exciting role offers the opportunity to work across a range of projects¿from curating datasets for retrospective AI evaluation to supporting large-scale prospective clinical trials.
We have created a large imaging dataset of screening mammograms, CC-MEDIA. It comprises over 250,000 screening mammograms with robust clinical metadata. It is currently being used to evaluate commercial AI tools for cancer detection and for predicting cancers that may develop within 3¿5 years following a negative mammogram. This role provides scope not only to support these evaluations but also to develop and test your own models.
As part of the role, you will help expand CC-MEDIA by collecting and curating screening data from sites using mammography systems from different vendors. This is a vital component of the EDITH trial (Early Detection using Information Technology in Health), which will recruit 660,000 women from across the UK. The Cambridge team is responsible for collecting mammograms for the comparison testing of AI tools at the end of recruitment.
You will also contribute to the development of an integrated approach to risk-stratified screening, following the success of the BRAID trial. This work will help inform NHS Breast Screening Programme policy, assessing the value of supplemental imaging based on AI-generated risk predictions.
Another major project supported by this role is ODELIA, a European collaborative trial using breast MRI and swarm learning. You will help curate MRI datasets, coordinate swarm learning experiments, and provide technical input to establish local machine learning capabilities.
The post offers opportunities to work closely with academic and industry partners, both within and beyond Cambridge. You will also be expected to contribute to the department's AI-related activities, including teaching, supervising student projects, and supporting medical student modules. Involvement in grant writing and participation in regular group and departmental meetings are key parts of this position. This is an excellent opportunity to join a collaborative, enthusiastic, and high-impact research team, with close links to Cambridge University Hospitals NHS Foundation Trust.
Fixed-term: The funds for this post are available until 31 December 2027 in the first instance.
Once an offer of employment has been accepted, the successful candidate will be required to undergo a health assessment. This appointment also requires a Research Passport application.
Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.
Applicants must have (or be close to obtaining) a PhD.
Appointment at Research Associate level is dependent on having a PhD. Those who have submitted but not yet received their PhD will initially be appointed as a Research Assistant (Grade 5, Point 38 £34,132) moving to Research Associate (Grade 7) upon confirmation of your PhD award.
Please ensure that you upload a covering letter and CV in the Upload section of the online application. The covering letter should outline how you match the criteria for the post and why you are applying for this role. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.
Please include details of your referees, including email address and phone number, one of which must be your most recent line manager.
Please quote reference RQ46735 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.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.
