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.
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).
At the University of Basel, you'll discover an inspiring environment with modern employment conditions, room to grow, and a culture that values diversity. Would you like to learn more? Visit our Working at the University of Basel page for detailed information.
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