Jobs

Study how Caenorhabditis nematodes sense their surroundings to build collective towers and hitch rides to new habitats, mainly in the lab with possible local fieldwork.

Investigate how senescent cells interact with reprogramming, immune cells and tissue remodelling, with a focus on female reproductive ageing, using mouse models, organoids and senolytic drugs.

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

Test the lab's new small-molecule inhibitors in cancer immunotherapy and autoimmune disease models, using in vivo work, flow cytometry and bioinformatics with industry partners.

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.

Apply generative models, inverse design and optimisation to engineering design problems, with links to robotic assembly and combining physical experiments with AI models.

Joint ETH and Children's Hospital Zurich project studying how neuroblastoma reshapes nutrients, blood vessels and immune cells around the tumour, using mouse models to find metabolic weak points.

Design and synthesise new ionisable lipids for lipid nanoparticles, then formulate and test them for delivering nucleic acid therapies in a nanomedicine lab.

Year-long practice-based study of how researchers publish, share and get credit for their work, involving a researcher survey, interviews with research teams and co-designed interventions.

Computational role analysing spatial transcriptomics from a mouse model of polycystic kidney disease to locate inflammatory niches and changes in cell signalling after JAK inhibitor treatment.

Wet-lab postdoc on an ARIA-funded project testing whether targeted epigenetic editing can switch on antiviral immune programmes in human cells, working with computational and chemistry collaborators.

Part of a Cancer Grand Challenges team, this post develops genome-wide methods to map DNA damage and track whether it is repaired, tolerated or becomes a mutation.

Help set up a new lab exploring how cell senescence after cancer therapy shapes anti-tumour T cell immunity in liver cancer, using in vivo models and genetic screens.

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.