Open Targets is based on the stunning Wellcome Genome Campus, home to some of the world's foremost institutes and organisations in genomics and computational biology. We work in dynamic teams at the interface of academic and pharma industry science on a crucial problem, how to be more successful in making drugs. Working with us, you will be exposed to new technologies and a dynamic set of scientists dedicated to translational research.
Our posts are usually in either one of our academic partners, The Wellcome Sanger Institute or EMBL-EBI and have terms and conditions associated with the employer.
Important note: applications may be reviewed on an ongoing basis and the advertised post(s) may be filled before the stated deadline.
The Petsalaki Group at EMBL-EBI is looking to recruit a Postdoctoral Fellow to work on a project to identify combination targets for KRAS-mutant colorectal cancer and triple negative breast cancer. You will be working in collaboration with the team to analyse the largest genetic interaction dataset generated to date in human cell lines (75K pairs across 50 cell lines) in order to prioritise candidate target pairs for therapeutic applications, understand the mechanisms and principles underpinning these genetic interactions. Additionally you will have the opportunity to develop a predictive model of drug synergy that is also interpretable in terms of mechanism of action.
The Human Technopole is seeking a highly motivated researcher to fill a Postdoctoral Fellow position in the group led by Francesco Iorio within the Centre for Computational Biology. The project aims to identify new oncology therapeutic targets and perform in silico drug prescriptions based on the analysis of functional genomics and patient data. More specifically, the role will develop new algorithms and methods (and will extend existing computational tools) for calibrating thousands of cancer in vitro models (encompassing cell lines and 3D organoids, across tens of tissue lineages and cancer types) onto suitably represented cancer patients' sub-populations, via integrated analyses of data from their multi-omics characterisations and clinical prior knowledge.