We are looking for a Postdoctoral fellow to develop computational approaches to integrate gene functional and physical interactions networks with target-disease association data with a view of studying how association evidence among network members can best be combined for disease phenotype predictions, pathway stratification and drug target identification. This post is a part of a project funded by the Open Targets initiative aiming at integrating association genetics and prior knowledge from protein/gene networks.
Our group is interested in studying genotype to phenotype associations by taking into account structural and cell biology knowledge. Within this broad scope we have, for example, been integrating different variant effect predictors in order to interpret the molecular consequences that are intermediate between genotype and phenotype. The applicant would aim to develop novel frameworks to integrate prior knowledge network information, large scale gene expression and protein abundance data across human samples, protein structural information, gene essentiality screens and large scale genomic association information (GWAS, eQTL and pQLT). You can learn more about our research group here.
Considered applicants must hold a PhD in Life Sciences and have previous experience in computational biology. The ideal candidate will have expertise in analysis of large-scale genomics data. Experience in functional association networks, structural bioinformatics or drug target predictions methods are considered an asset. The post holder should enjoy working in a highly collaborative and interdisciplinary environment. Good written and oral communication skills are essential.