PhD position in Physics-Inspired AI for Drug Design

Date: Dec 12, 2025

Location: Department of Pharmaceutical Sciences | Klingelbergstrasse 50 | 4056 Basel | Switzerland, CH

Company: Universität Basel

100% / Available: February 2026

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 (https://doi.org/10.1038/s41467-025-63947-5).
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

Your assignments

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.

Your profile

  • 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.

We offer you

  • PhD student position.
  • Training into the key methods of an emerging research field.
  • International and collaborative research environment.


Application / Contact

Please submit your complete application documents, including

  • Letter (max. 1 page) highlighting motivation, experience and skills
  • CV
  • Diploma of Bachelor’s and Master’s degree
  • Contact details of at least two academic references 

via the online recruiting platform.

 

Position is available immediately. You can find out more about us at https://pharma.unibas.ch/de/research/research-groups/computational-pharmacy-2155/ .

For questions, please contact Prof. Markus Lill (markus.lill@unibas.ch).