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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 position

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 ([email protected]).

Apply

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