Job Description

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 ( ).
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:
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 .
For questions, please contact Prof. Markus Lill ( ).

Skills Required