The University of Basel is offering a fully funded PhD position in the Computational Pharmacy group, focusing on the development of physics-inspired AI methodologies for drug design. This research opportunity is ideal for candidates interested in integrating physicochemical principles with advanced deep learning techniques to address challenges in protein–ligand interaction modeling. The group’s recent work has highlighted the limitations of purely data-driven AI models in life sciences, emphasizing the need for approaches that combine data with physical modeling.

The successful candidate will join an international and collaborative research environment, contributing to the creation of next-generation docking frameworks that explicitly incorporate protein–ligand dynamics. Responsibilities include designing and implementing innovative deep neural network models, integrating physical principles and molecular modeling knowledge into learning architectures, and collaborating with experimental research groups for real-world validation of developed algorithms.

Applicants should possess an MSc in Physics, Computational Chemistry, or Computer Sciences, with excellent knowledge in statistical mechanics and thermodynamics. Research experience, preferably with publications, strong Python programming skills, and expertise in machine learning—particularly neural network concepts—are required. Fluency in English and the ability to work effectively in a team are essential.

The position is fully funded, covering tuition and stipend, and offers training in key methods of an emerging interdisciplinary field. The application deadline is January 11, 2026, and the position is available immediately. Interested candidates should submit a motivation letter (max. 1 page), CV, diplomas of Bachelor's and Master's degrees, and contact details of at least two academic references via the online recruiting platform. For further information about the group, visit the Computational Pharmacy group’s webpage. Questions can be directed to Prof. Markus Lill at [email protected].

This is an excellent opportunity for highly motivated individuals seeking to advance their expertise at the intersection of physics, chemistry, computer science, and pharmacy, and to contribute to innovative research in drug design using AI.