We are looking for an enthusiastic postdoctoral fellow to join our collaborative project. The Petsalaki group uses network analysis, data integration and machine learning to study human cell signalling.

For this project, which is funded by Open targets, we are collaborating with the Garnett group (Sanger institute) and industry partners such as GSK and Sanofi, to discover combination targets for two types of cancer with unmet need. We have already finished collecting the largest dual CRISPR guide dataset to date comprising 65,000 gene pairs across two tissue types and approximately 50 cell lines. We are therefore at the most exciting time in the project, and are looking to understand principles underpinning successful combination gene pairs in cancer and to discover actionable gene pairs that could have an impact in the clinic.

Your role
For this post the selected fellow will be integrating this unique and exciting data with other omics datasets, performing systems level and network-based analyses to interpret and extract value from the data. They will collaborate with the experimental colleagues at the Sanger institute and with the industry partners for this analysis and to aid in-depth mechanistic follow up studies of selected gene pairs.

They will also be developing an AI-based method (e.g. through graph neural networks) to leverage our unique dataset for the prediction of combination targets beyond the ones measured in our experiments.

You have
A PhD in bioinformatics, statistics or related discipline.
Extensive experience in python or R programming, network analysis, bioinformatics
Excellent communication and collaborative skills

You might also have
Familiarity working with CRISPR data
Experience in AI/machine learning