Markus Lill

The University of Basel's Computational Pharmacy Group is offering a fully funded PhD position in Physics-Inspired AI for Drug Design. This interdisciplinary research opportunity integrates quantum mechanics, molecular dynamics, and deep learning to advance pharmaceutical discovery. The project focuses on embedding physicochemical principles into neural networks to predict protein-ligand interactions, enabling the design of novel drug candidates and transforming precision medicine and therapeutic development. The successful candidate will join a dynamic team at the University of Basel, Switzerland, working under the supervision of Prof. Markus Lill. The position is fully funded and offers 100% employment, with a start date in February 2026. The research will involve advanced computational methods, including statistical mechanics, thermodynamics, and machine learning, with a strong emphasis on Python programming and neural network development. Applicants should possess an MSc in Physics, Computational Chemistry, or Computer Sciences, with excellent knowledge in statistical mechanics and thermodynamics. Strong programming skills in Python, experience in machine learning and neural networks, and a proven research track record (preferably with publications) are required. Fluency in English and a collaborative, motivated attitude are essential. To apply, candidates must prepare a motivation letter (max. 1 page), CV, copies of Bachelor's and Master's diplomas, and contact details for at least two academic references. Applications should be submitted via the University of Basel's online recruiting platform. For further information or questions, applicants may contact Prof. Markus Lill at [email protected]. More details and the application portal can be accessed through the provided links. This position is ideal for candidates interested in computational pharmacy, physics-inspired artificial intelligence, and drug discovery, offering a unique opportunity to contribute to cutting-edge research in a leading European institution.

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.

Postdoctoral Position in AI-Driven Drug Design at the University of Basel, Switzerland. The Computational Pharmacy group is seeking a postdoctoral researcher to work on next-generation artificial intelligence approaches for drug design. The project sits at the interface of AI, cheminformatics, computational chemistry, and molecular modeling, and is embedded in an international Innosuisse research collaboration focused on closed-loop drug discovery. The role centers on building an integrated Design–Make–Test–Analyze (DMTA) platform that combines generative AI, ultra-large synthetically accessible chemical spaces, physics-informed molecular representations, off-target prediction, and experimental feedback. The methods developed in the project will be used in iterative prospective drug-discovery cycles, with a serine protease from the complement system as a real-world lead-optimization case study. The successful candidate will develop and adapt machine-learning approaches for structure-based and generative molecular design, incorporate physicochemical and protein–ligand interaction information, and create computational workflows for closed-loop optimization based on affinity, selectivity, and molecular-property data. The project also involves prospective validation, publication, presentation, and reporting, together with close collaboration with computational scientists, chemists, biologists, and industrial partners. Applicants should hold a PhD in Computational Chemistry, Cheminformatics, Computer Science, Physics, or a related discipline. Required experience includes strong machine learning/deep learning skills, excellent Python programming ability, and familiarity with at least one of: molecular generative AI, cheminformatics and molecular representations, or structure-based drug design/protein–ligand modeling. Knowledge of molecular modeling and the physicochemical basis of molecular recognition is highly desirable, alongside a strong publication record and fluent English communication skills. The position is fully funded and available immediately. Applications must be submitted through the University of Basel online recruiting platform. To apply, prepare a motivation letter (max. 1 page), CV with publication list, PhD certificate or confirmation of expected completion, and contact details for at least two academic references. Questions may be directed to Prof. Markus Lill at [email protected].