Research Assistant / Data Scientist in Artificial Intelligence for Computational Cancer

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 nursery
• On-site gym
• Enhanced maternity, paternity and adoption pay
• Employee assistance programme: Staff Support Service
• Discounted medical insurance

Please visit https://www.ucl.ac.uk/work-at-ucl/rewards-and-benefits to find out more.

About us

This post is based in Professor Gert Attard's Treatment Resistance group at the UCL Cancer Institute and Dr. Tapabrata Rohan Chakraborty's Transparent and Reliable Lab (TRAIL) in UCL Dept of Medical Physics and Biomedical Engineering.

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. Professor Attard's research group utilises bioinformatic analysis, computational modelling, and digital histopathology to interrogate treatment resistance in prostate cancer, and he leads several of the world leading trials in prostate cancer, including STAMPEDE, that have changed clinical practice multiple times.

Dr. Chakraborty's research group TRAIL is a computational lab that develops state of the art explainable and trustable AI/ML methods applied to grand challenges in personalised healthcare and precision biomedicine, particularly multimodal computational cancer problems. UCL Medical Physics & Biomedical Engineering is a world-leading multidisciplinary department that develops innovative technologies spanning medical imaging, biomedical engineering, healthcare technologies, artificial intelligence and computational medicine.

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. We are seeking a highly motivated and talented Data Scientist (Grade 7) in AI for computational cancer to join an ambitious and multidisciplinary research programme focused on constructing and validating transparent, interpretable, and biologically grounded models directly from multimodal cancer datasets.

About the role

The successful candidate will lead the research, generation, and exploration of novel multi-modal AI models, intended to deepen our understanding the underlying relationships between different forms of computational cancer data - including digital histopathology, immunohistochemistry, and genomics - generated from tissue samples collected as part of prostate cancer clinical trials. The researcher 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 to develop and apply cutting-edge AI technologies to challenging real-world problems in biomedicine.

For more details about the Attard Group, please visit:

Treatment Resistance | Faculty of Medical Sciences

This post is funded for 1 year in the first instance, with potential of extension subject to availability of funds.

Applications should include a CV and a Cover Letter: In the Cover Letter, please provide evidence of the essential and desirable criteria in the Person Specification part of the Job Description.

About you

You will have a strong computational background, with an MSc or equivalent in Artificial Intelligence, Machine Learning, Data Science, Computer Science, Engineering or a related discipline, together with experience developing and applying AI and machine learning methods to complex research challenges. You will possess excellent programming skills, particularly in Python and modern machine learning frameworks such as PyTorch or TensorFlow, and have experience in areas such as computer vision, natural language processing or multimodal AI.

You will be an analytical and innovative researcher with excellent problem-solving skills and, ideally, experience applying AI to healthcare or computational cancer research. Knowledge of cancer biology, multimodal biomedical data, genomics, imaging or digital pathology would be advantageous.

The successful candidate will be highly organised, able to work independently and committed to producing high-quality, reproducible research. You will have strong communication skills and enjoy collaborating with clinicians, biologists, data scientists and researchers from diverse backgrounds. Most importantly, you will be passionate about applying cutting-edge AI technologies to advance cancer research and improve patient outcomes.

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.

You can read more about our commitment to Equality, Diversity and Inclusion here: https://www.ucl.ac.uk/equality-diversity-inclusion/