Senior Research Fellow – Digital Pathology & AI
The UCL Cancer Institute is the hub for cancer research at University College London, one of the world's leading universities. The Institute draws together over 400 talented scientists who are working together to translate research discoveries into developing more effective therapies for cancer patients. This work is facilitated by extensive collaborations with our partner hospital UCLH, supported by the UCLH Biomedical Research Centre. As part of the CRUK City of London Centre, scientists at the Cancer Institute have access to cutting‑edge research facilities and clinical and non‑clinical graduate programmes.
Within the Cancer Institute, researchers in the Department of Cancer Biology and Pathology seeks to identify and understand the molecular mechanisms and disease processes that underlie cancer development and treatment. Our research is facilitated by expertise in genomics, cell biology and digital medicine and by close collaborations between clinical and non‑clinical colleagues. Further Information about the department and the UCL Cancer Institute can be found on our website https://www.ucl.ac.uk/cancer/.
About the role
The UCL Cancer Institute—one of the world’s leading cancer research centres—is seeking an exceptional Senior Research Fellow to lead innovative work at the intersection of digital pathology, AI, and real‑world clinical data.
This is a unique opportunity to develop an independent research programme focused on applying machine learning to digital pathology data and integrating it with electronic health records to drive new insights in cancer biology and patient care.
Working within a highly collaborative environment—including partnership with UCL Hospital - you will drive cutting‑edge research that translates data into real clinical impact.
Role highlights:
* Develop an independent research programme applying machine learning to pathology & EHR data
* Work with large‑scale clinical datasets to drive translational cancer research
* Collaborate with clinicians, data scientists, and NHS partners.
* Publish high‑impact research and secure external funding
Applications should include a CV and a Cover Letter: In the Cover Letter please evidence the essential and desirable criteria in the Person Specification part of the Job Description. (By including a Cover Letter, you can leave blank the 'Why you have applied for this role' field in the application form, which is limited in the number of characters it will allow.)
The position is for 18 months in the first instance, with the possibility of extension subject to funding.
About you
Prospective candidates must have:
* PhD + postdoctoral experience in digital pathology / AI / computational biology
* Expertise in digital pathology, machine learning and healthcare data
* Publication record and ready to lead your own independent research
* Excellent written and oral communication skills (fluent in spoken and written English)
* The highest ethical and professional standards in research and education.
What we offer
As well as the exciting opportunities this role presents we also offer some great benefits some of which are below:
* 41 Days holiday (including 27 days annual leave 8 bank holiday and 6 closure days)
* Defined benefit career average revalued earnings pension scheme (CARE)
* Cycle to work scheme and season ticket loan
* On‑site gym
* Enhanced maternity, paternity and adoption pay
* Employee assistance programme: Staff Support Service
Our commitment to Equality, Diversity and Inclusion
As London’s Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world’s talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong.
We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL’s workforce.
These include people from Black, Asian and ethnic minority backgrounds; disabled people; LGBTQI+ people; and for our Grade 9 and 10 roles, women.
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