Research Fellow​/Senior Research Fellow in Artificial Intelligence Cancer Digital Twins

Research Fellow / Senior Research Fellow in Artificial Intelligence for Cancer Digital Twins

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

Senior Research Fellow in Artificial Intelligence to join an 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 AI technologies that integrate machine learning, mechanistic modelling, formal verification and large-scale biomedical data.

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 oftumour 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 develop novel neurosymbolic AI methodologies that combine the power of large language models, and machine learning with formal reasoning, mechanistic modelling and formal verification to automatically construct and validate transparent, interpretable biologically grounded models directly from large-scale cancer datasets and scientific knowledge.

The successful candidate will play a central role in the design and development of a neurosymbolic AI platform for cancer. They will contribute to the integration and analysis of large-scale multi-omics and clinical cancer datasets, the development of AI models and the creation of explainable and verifiable computational frameworks for tumour digital twins. Working at the intersection of artificial intelligence, formal methods and cancer research, the postholder will collaborate closely also with Professor Mateja Jamnik and her group in the Department of Computer Science and Technology at University of Cambridge, alongside biologists, clinicians and computer scientists from partner organisations.

The research will contribute to fundamental advances in our understanding of cancer biology while supporting the development of innovative approaches for patient stratification, therapeutic target discovery, treatment optimisation and precision oncology. The successful candidate will have the opportunity to publish in leading journals, present their work at major international conferences and contribute to the development of transformative AI technologies with significant scientific and clinical impact.

This is an exceptional opportunity for an ambitious AI researcher who is passionate about applying cutting-edge AI technologies to challenging real-world problems in biomedicine. The position offers the chance to work at the forefront oftrustworthy AI, neurosymbolic reasoning, computational biology and cancer systems medicine, within a world-leading research environment committed to scientific excellence and translational impact.

Appointment at Grade 7 and Grade 8 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.

Appointment at Grade 8 is contingent on the candidate’s previous experience.

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