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. Representative publications from the group demonstrate their expertise in computational chemistry, biophysics, and machine learning.

The successful candidate will contribute to the creation of next-generation docking frameworks that explicitly incorporate protein–ligand dynamics, leveraging both physics-based modeling and state-of-the-art neural network architectures. Responsibilities include designing and implementing innovative deep neural network models, integrating molecular modeling knowledge into learning architectures, and collaborating with experimental research groups for real-world validation of developed algorithms.

Applicants should hold an MSc in Physics, Computational Chemistry, or Computer Science, with excellent knowledge in statistical mechanics and thermodynamics. Research experience, preferably with publications, is highly valued. Strong programming skills in Python and experience in machine learning, particularly neural network concepts, are essential. Candidates must possess fluent English communication skills and demonstrate motivation and teamwork abilities.

The position offers comprehensive training in emerging computational drug design methods within an international and collaborative research environment. Funding is fully provided, covering tuition and stipend. The application deadline is February 13, 2026, and the position is available immediately. Interested candidates should submit a motivation letter, CV, diplomas, and contact details of at least two academic references via the online recruiting platform. For further information about the group and research focus, visit the Computational Pharmacy group’s webpage. Direct inquiries can be sent to Prof. Markus Lill at [email protected].