Jobs

Develop new machine learning methods for single-cell sequencing data in a team that pairs computational method development with biological collaborators.

Build neural network models that describe how a single cell's molecular state changes over space and time, combining imaging with molecular data under biological constraints.

Lead an institute-wide framework for reproducible code and responsible AI use in cancer research, extending existing research integrity practice to computational work alongside the research integrity lead.

Build machine learning models that combine NHS clinical, genomic, imaging and pathology data to predict outcomes, including a programme on myeloproliferative neoplasms, a group of blood cancers.

Join a European project building a multi-agent AI system for ovarian cancer that fuses imaging, genomics, pathology and clinical records via federated learning, with work on interpretable tools for clinicians.

Develop machine learning methods, including graph neural networks and generative models, on spatial genomics data to understand how mutant cell clones interact with tissue in chronic liver disease and liver cancer.

In-house editor for PeerJ Computer Science, handling escalated peer-review cases, managing the editorial board, leading ethics investigations and commissioning content. A parallel posting exists for Abingdon.

Editor for machine-learning research at Nature Communications, making manuscript decisions, overseeing peer review and commissioning content. Based in India or China (Pune, New Delhi, Shanghai, Beijing or Nanjing).