We are seeking a motivated and creative PhD student to develop the next generation of AI-driven protein design methods that explicitly account for protein–lipid and protein–membrane interactions. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and nanopores. These methods will open the door to engineering sophisticated functionalities into synthetic membrane proteins - including selective transport, sensing, and signal transduction.

Research environment

The at the (co-affiliated with the VIB Center for AI & Computational Biology) pioneers methods for the de novo design of transmembrane β-barrel nanopores. The group provides a highly interdisciplinary environment where computational method development is closely integrated with experimental validation. The successful candidate will have opportunities ...