Machine Learning-Guided Directed Evolution
Evolution is an all-purpose problem solver, which researchers mimic in the laboratory to engineer tailor-made (bio)molecules that aid us in combating diseases and in realizing a sustainable economy. While effective, such directed evolution campaigns are not only laborious and time-consuming, but also cover only a miniscule fraction of the unimaginably large sequence space available. As a result, means to guide evolutionary trajectories along a biomolecule’s fitness landscape are sought-after, as they could greatly accelerate evolutionary searches.
Position Details
Position: PhD Student
Start Date: Between June 2025 and October 2025
Within the framework of the ML-GUIDE project, we will make directed evolution guidable and, ultimately, predictable by machine learning. Specifically, we will build a first-in-class framework to expedite the design of efficient catalysts for synthetic applications, focussing on the development of efficient oligopeptide catalysts for carbon-carbon bond-forming reactions. By seamlessly merging cutting-edge directed evolution, next-generation sequencing, and deep learning approaches, we will establish accelerated Design-Build-Test-Learn cycles to continuously improve models via active learning and guide evolutionary trajectories toward promising but otherwise inaccessible sequence spaces. To apply, please submit your application material through the platform linked below.
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