Postdoc in Epistatic-Aware Machine Learning for Protein Engineering - DTU Compute

Job description

The postdoc will develop machine learning for protein engineering, including: (1) investigating how epistatic effects in proteins can be captured by machine learning models; (2) develop machine learning models for protein engineering that are able to optimize additive as well as epistatic effects. The work will be in collaboration with domain experts who will be working on optimizing an NADPH-dependent enzyme called formate dehydrogenase. Optimizing this enzyme will be a model system used to evaluate the developed methods. You will join a highly motivated and collaborative team under the supervision of Søren Hauberg, Wouter Boomsma, and Carlos G. Acevedo-Rocha. The research environment favors creative ‘slow thinking’ style research and we emphasize having fun along the way. Our work is consistently published at the top venues of machine learning and protein research. Responsibilities and qualifications: You should have prior experience with machine learning from both a theoretical and practical perspective. Experience in the following topics will be appreciated: Basic understanding of proteins and biology is also an advantage. We tend to work in teams, so a collaborative spirit is...