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.

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].

The University of Basel is offering a fully funded PhD position in the Computational Pharmacy group, focusing on the development of physics-inspired artificial intelligence (AI) methods for drug design. This research aims to advance next-generation drug discovery by integrating physicochemical principles directly into deep neural network models, addressing the limitations of purely data-driven approaches in modeling protein–ligand interactions. The group has a strong publication record in this area, with recent work demonstrating the need for more generalizable AI models in life sciences. The successful candidate will contribute to ongoing projects that combine physics-based modeling with state-of-the-art machine learning techniques. The main objective is to create a next-generation docking framework that explicitly incorporates protein–ligand dynamics, enabling more accurate predictions and real-world validation through collaboration with experimental research groups. Responsibilities include designing and implementing innovative deep neural network architectures, integrating physical principles and molecular modeling knowledge, and working closely with interdisciplinary teams. Applicants should have a Master’s degree in Physics, Computational Chemistry, or Computer Sciences, with excellent knowledge in statistical mechanics and thermodynamics. Research experience, preferably with publication, is expected, along with strong programming skills in Python and familiarity with machine learning, especially neural network concepts. Fluency in English and a collaborative, motivated attitude are essential. The position offers training in key methods of an emerging research field, access to an international and collaborative research environment, and the opportunity to work at the forefront of computational drug design. The PhD is fully funded, covering tuition and stipend. The position is available immediately, and applications should be submitted via the online recruiting platform. Required documents include a motivation letter, CV, diplomas, and contact details for at least two academic references. For further information, candidates can visit the Computational Pharmacy group’s website or contact Prof. Markus Lill directly. Application deadline: 5 June 2026. For more details and to apply, visit the official application link.