PhD position in Physics-Inspired AI for Drug Design

Neural network models have transformed many areas of life sciences, including protein structure prediction and molecular generation. However, due to limited high-quality data, purely data-driven AI models often lack the generalizability required to reliably model protein–ligand interactions, as recently demonstrated by our group ().

Our research therefore focuses on advancing next-generation drug design methodologies by integrating physicochemical principles directly into deep neural network approaches. Representative publications from our group include :

https : / / doi.org / 10.1021 / acs.jcim.2c01436

https : / / doi.org / 10.1021 / acs.jcim.1c01438

https : / / icml-compbio.github.io / 2023 / papers / WCBICML2023_paper159.pdf

https : / / doi.org / 10.1038 / s42004-020-0261-x

A fully funded PhD position is available in the Computational Pharmacy group at the University of Basel. The successful candidate will contribute to ongoing research on the development of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework that explicitly incorporates protein–ligand dynamics.

You will be responsible for :

• Designing and implementing innovative deep neural network models.
• Integrating physical principles and molecular modeling knowledge into learning architectures.
• Collaborating with experimental research groups, enabling real-world validation and application of newly developed algorithms.
• MSc in the fields of Physics, Computational Chemistry or Computer Sciences.
• Excellent knowledge in Statistical Mechanics & Thermodynamics.
• Research experience preferably with publication.
• Strong programming skills in Python.
• Experience in machine learning, in particular neural network concepts.
• Fluent verbal and written communication skills in English.
• Highly motivated, interactive team player.
• PhD student position.
• Training into the key methods of an emerging research field.
• International and collaborative research environment.

Application /