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

Organisation/Company UCL Cancer Institute Department Cancer Institute Research Field Technology Researcher Profile First Stage Researcher (R1) Positions Research Support Positions Final date to receive applications 28 Oct 2026 - 16:06 (Europe/London) Country United Kingdom Type of Contract Temporary Job Status Full-time Hours Per Week 36.5 Offer Starting Date 1 Oct 2026 Is the job funded through the EU Research Framework Programme?

Other EU programme Is the Job related to staff position within a Research Infrastructure? No

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.

You will have a PhD, or be close to completing one, in Computational Biology, Bioinformatics, Cancer Biology, Systems Biology, Genomics or a related discipline, with experience analysing large-scale biomedical datasets, including genomic, transcriptomic and multi-omics data. You will have strong programming skills in R, Python and MATLAB, alongside expertise in statistical analysis, data integration and reproducible research practices.

You will be an innovative and highly motivated researcher with excellent analytical and problem-solving skills, capable of working independently while contributing effectively to a multidisciplinary team. Experience in cancer biology, computational oncology, bioinformatics or related fields is essential, and a strong publication record demonstrating high-quality research outputs is expected.

You will be an excellent communicator with the ability to present complex scientific concepts to a range of audiences and collaborate effectively with biologists, clinicians, computational scientists and AI researchers. Organised, proactive and committed to scientific excellence, you will be excited by the opportunity to contribute to pioneering research at the forefront of computational cancer biology, tumour digital twins and precision medicine.

Specific Requirements

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,…