PhD position in Physics- AI Drug Design

Job Overview

Organisation/Company University of Basel Research Field Chemistry » Computational chemistry Computer science » Other Physics » Biophysics Physics » Computational physics Physics » Thermodynamics Researcher Profile First Stage Researcher (R1) Country Switzerland Final date to receive applications 11 Jan 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure?

No

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 ((Use the "Apply for this Job" box below).). 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: .
2c01436 .
1c01438  .

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.

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

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