What are we looking for? One way in which cells adapt diverse functional responses is through context-dependent interactions with their microenvironment. Cells typically send messages to their microenvironment by emitting ligands, which bind to complimentary receptors on the surfaced of target cells; then triggering a change in the behavior of the target cell. Complimentary ligand-receptor-pairs are well annotated and methods have been developed to infer cell-cell interactions from single cell expression data based on their mutual expression. However, existing work views each cell as an independent unit. We have taken the alternative view that cells sampled from the same tumor microenvironment (TME) are fundamentally participating in an underlying biological process and are working towards generating a principled understanding of how cells coordinate their behavior; which has the potential to unlock deep biological insights into cancer progression, and suggest new targets for effective treatments. The successful candidate will be highly independent and benefit from a background in: deep learning and graphs, variational inference, dynamical systems, network analysis, computational/systems biology. Cellular function is also complicated by the frequency of pleiotropy found in biological systems; that is when one gene influences many traits. We are developing modular high-throughput technologies to map the function of each building block (or residue) of a protein at scale in different contexts. Towards this, we are recruiting candidates with expertise in functional genomics, synthetic biology and protein degradation/trafficking. No background in cancer biology is necessary! We value learning from individuals that come from diverse training backgrounds.