3 PhD Positions in ML-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.

Within the framework of the recently funded ML-GUIDE project, we will make directed evolution guidable and, ultimately, predictable by machine learning. Specifically, you will build a first-in-class framework to expedite the design of high-affinity binders that engage with therapeutic targets or efficient (bio)catalysts for synthetic applications. By seamlessly merging cutting-edge directed evolution, next-generation sequencing, and deep learning approaches, you 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.

You will be embedded in one of the three research groups involved in the ML-GUIDE project and focus your efforts on guiding engineering efforts for one particular biomolecule and its associated function.