Computational Biologist, Immune Cell Repolarization

Develop machine-learning, statistical and multi-omics methods in Aleksandar Obradovic's Immune Cell Re-Programming group to understand resistance to checkpoint-inhibitor immunotherapy. Projects analyse transcriptional, TCR-seq and spatial data from clinical and preclinical samples to build mechanistic models of tumour-immune-stromal crosstalk and prioritise combination therapies and regulatory targets for reprogramming T cells, macrophages and fibroblasts.

PhD in systems biology, AI/machine learning or statistics, or an MS with relevant experience, plus 1-2 years of biomedical research showing understanding of cell biology, transcription and signal transduction. Programming in R and Python; building and evaluating machine-learning or neural network models on biological data, including probabilistic or deep models such as RNNs, GNNs or protein sequence models.