The Open Targets team at our 10 year anniversary celebration in October 2024
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.
Help create an open, community-driven platform that transforms how researchers interpret biological networks and identify new therapeutic targets.
You will benchmark and integrate state-of-the-art network contextualisation methods, combining large-scale transcriptomics, proteomics, and single-cell data across key disease areas including oncology, inflammatory bowel disease, and neuroinflammation.
Develop a comprehensive open source side effect resource for the scientific and pharmaceutical community, and provide structured and standardised training sets for AI/ML applications to improve early identification of safety liabilities.
You will harness modern Natural Language Processing (NLP) techniques to extract data from a range of relevant resources, such as clinical trials, publications and drug labels. You will work closely with team members to ensure development of automated pipelines and effective integration into the ChEMBL database and Open Targets Platform, to assist users in the selection of safe and efficacious drug targets.
You will perform large-scale phenotypic CRISPRn screens on human iPSC-derived microglia to identify modifiers of immune dysfunction relating to neurodegeneration, contributing to a unique, cross-comparative analysis integrating cellular models and patient-derived data.
Join our interdisciplinary team at the forefront of computational biology and AI. Lead or contribute to transformative projects that integrate single-cell genomics, spatial transcriptomics, and generative AI to build next-generation models for understanding tissue biology and cellular dynamics across organs such as the pancreas, kidney, skin, and liver.
Available research focus areas include: space and multi-omics atlas construction, generative AI for cell fate and perturbations, foundational models for single-cell biology, Open Targets translational AI projects, agentic AI for scientific reasoning and experiment design, core machine learning research, multimodal learning, leap project.