Research Fellow in Computational Cancer Biology - Part-time FTE

Research Fellow in Computational Cancer Biology - Part-time 0.8 FTE

This post is based in Professor Jasmin Fisher’s laboratory at the UCL Cancer Institute
. The UCL Cancer Institute is a world-leading centre for cancer research, bringing together more than 400 scientists and clinicians who work collaboratively to understand cancer and translate discoveries into improved diagnostics, treatments and patient outcomes. .

We are seeking a highly motivated and talented Postdoctoral Research Fellow in Computational Cancer Biology to join the Fisher Lab’s ambitious and multidisciplinary research programme focused on understanding cancer using AI to generate digital tumour twins. This programme aims to transform cancer research and precision oncology by developing a new generation of transparent, interpretable and trustworthy artificial intelligence technologies that integrate large-scale biological and clinical data with mechanistic models of cancer.

Cancer is a complex, multi-scale disease involving interacting processes across the molecular, cellular, tissue and organ levels. At the same time, advances in genomics, molecular profiling, pathology, imaging and clinical data collection have generated unprecedented volumes of multimodal cancer data. A major challenge is to integrate these diverse data sources into coherent computational frameworks that can generate biological insights, support clinical decision-making and accelerate therapeutic discovery.

The Fisher laboratory addresses this challenge through the development of tumour digital twins, which are executable mechanistic models that capture the biological behaviour of individual tumours and can be used to predict disease progression and treatment response. A central goal of the programme is to combine advances in generative AI, computational biology and formal verification to automatically construct transparent, logic-based models directly from large-scale cancer datasets, while ensuring that these models are robust, explainable and mathematically validated.

The successful candidate will play a key role in the integration and analysis of multi-omics and clinical cancer datasets, contributing to the development and validation of AI-enabled tumour digital twins. Working at the interface of cancer biology, bioinformatics, machine learning and computational modelling, the postholder will collaborate closely with a multidisciplinary team of biologists, clinicians and computer scientists. The work will contribute to the discovery of novel therapeutic targets, the identification of patient-specific treatment strategies and the development of trustworthy AI technologies for precision cancer medicine.

This is an exciting opportunity for a computational biologist or a bioinformatician who is passionate about combining cutting-edge data science with biological discovery and translational impact, and who wishes to contribute to a research programme at the forefront of cancer systems biology and trustworthy AI.

Appointment at Grade 7 is dependent upon the successful award of a PhD
. Candidates who have not yet been awarded their PhD may be appointed initially at Research Assistant Grade 6B (with progression to Grade 7 and backdated salary adjustment upon submission of the final corrected PhD thesis.

This post is funded for3 years in the first instance, with aprobationary period of 9 months.

The position is available from 1 st October 2026
, and early availability would be advantageous.

Interviews will be held in September 2026.

You will have a PhD, or be nearing completion of a PhD, in Computational Biology, Bioinformatics, Cancer Biology, Systems Biology, Genomics or a related discipline, together with experience analysing large-scale biomedical and multi-omics datasets.

You will possess strong programming skills in R, Python and MATLAB, excellent analytical and problem-solving abilities, and experience applying computational approaches to biological research. Experience in cancer biology, computational oncology or bioinformatics is essential, while familiarity with AI and machine learning approaches would be advantageous.

You will be an…