Location:Barcelona (Spain),Toronto (Canada), Gothenburg (Sweden), Cambridge (UK), Gaithersburg (USA).
You will be part of an interdisciplinary team (in partnership with the Biologics Engineering) that is responsible for the discovery and optimization of next generation biological drug candidates for all the key therapy areas across AstraZeneca.
You will be working on the design and development of a cyclic discovery process for biologics engineering based on active learning/optimization/search (machine learning models inform the design of wet-lab experiments, the wet-lab automation generates new high-throughput data that is used for model re-training and update of the hypothesis informing the next design step), as well as development of deep learning algorithms for virtual screening of antibodies (supporting the efforts for in silico lead identification and de novo design of antibodies).
You will design, implement, test, and analyze machine learning algorithms to help contribute to the overall improvement and automation of the pipeline for biologics engineering. You will be required to interact extensively with other teams across the organization, and our academic partners with the goal of delivering products in a timely manner.
It is expected that you will present to various partners, represent us at conferences, and publish your findings in scientific journals or top conferences such as ICML, ICLR, NeurIPS, etc.