Universität Basel
Basel
Postdoctoral Position in AI-Driven Drug Design
- 25 août 2026
- 100%
- 4000 Basel
À propos de cette offre
Artificial intelligence is rapidly transforming molecular design and drug discovery. However, the identification of successful drug candidates requires more than generating molecules with high predicted affinity: selectivity, physicochemical properties, potential adverse effects, synthetic accessibility, and experimental feedback must be considered simultaneously.
Our research in the Computational Pharmacy group at the University of Basel focuses on developing next-generation AI approaches for drug design by combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include:
https://doi.org/10.1038/s41467-025-63947-5
https://doi.org/10.1021/acs.jcim.2c01436
https://doi.org/10.1021/acs.jcim.1c01438
https://doi.org/10.1038/s42004-020-0261-x
Our research in the Computational Pharmacy group at the University of Basel focuses on developing next-generation AI approaches for drug design by combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include:
https://doi.org/10.1038/s41467-025-63947-5
https://doi.org/10.1021/acs.jcim.2c01436
https://doi.org/10.1021/acs.jcim.1c01438
https://doi.org/10.1038/s42004-020-0261-x
Your position
A fully funded Postdoctoral position is available in the Computational Pharmacy group at the University of Basel within an international Innosuisse research project on AI-driven closed-loop drug discovery.
The project aims to establish an integrated Design-Make-Test-Analyze (DMTA) platform combining generative AI, ultra-large synthetically accessible chemical spaces, physics-informed molecular representations, off-target prediction, and experimental feedback. The developed methods will be applied in iterative prospective drug-discovery cycles, with a serine protease from the complement system serving as a real-world lead-optimization case study.
The successful candidate will play a central role in the computational and AI components of the project and work closely with our international and industrial project partners.
You will be responsible for:
The project aims to establish an integrated Design-Make-Test-Analyze (DMTA) platform combining generative AI, ultra-large synthetically accessible chemical spaces, physics-informed molecular representations, off-target prediction, and experimental feedback. The developed methods will be applied in iterative prospective drug-discovery cycles, with a serine protease from the complement system serving as a real-world lead-optimization case study.
The successful candidate will play a central role in the computational and AI components of the project and work closely with our international and industrial project partners.
You will be responsible for:
- Developing and adapting machine-learning approaches for structure-based and generative molecular design.
- Integrating physicochemical information, including protein-ligand interaction features, into generative AI workflows.
- Developing computational workflows for closed-loop DMTA cycles in which experimental affinity, selectivity, and molecular-property data are continuously used to improve the next generation of proposed molecules.
- Applying and validating the developed approaches prospectively in the design and optimization of serine protease inhibitors.
- Collaborating closely with computational scientists, chemists, and biologists within the international project consortium.
- Contributing to scientific publications, presentations, and project reporting.
Your profile
- PhD in Computational Chemistry, Cheminformatics, Computer Science, Physics, or a related discipline.
- Strong background in machine learning and deep learning.
- Strong programming skills, particularly in Python.
- Experience in at least one of the following areas:
- molecular generative AI,
- cheminformatics and molecular representations,
- structure-based drug design and protein-ligand modeling,
- Experience with molecular modeling and a good understanding of the physicochemical principles governing molecular recognition is highly desirable.
- A strong publication record in internationally recognized, high-quality venues is required, such as leading journals in computational chemistry (e.g., JCTC, Journal of Chemical Physics) or top-tier machine-learning conferences (e.g., ICLR, ICML, NeurIPS), as appropriate to the candidate's research background.
- Fluent verbal and written communication skills in English.
- Highly motivated, independent, and collaborative researcher with an interest in working at the interface between methodological development and prospective drug discovery.
We offer you
- A Postdoctoral position in an interdisciplinary research project at the interface of artificial intelligence and drug discovery.
- The opportunity to develop new computational methodologies and directly test them in prospective Design-Make-Test cycles.
- Close interaction with experimental drug-discovery researchers and industrial and international project partners.
- An international and collaborative research environment at the University of Basel.
Application / Contact
Please submit your complete application documents, including:
The position is available immediately.
You can find out more about our research at:
https://pharma.unibas.ch/de/research/research-groups/computational-pharmacy-2155/
For questions, please contact Prof. Markus Lill (Write an email).
Please submit your complete application documents, including:
- motivation letter (max. 1 page) highlighting your research interests, relevant experience, and skills,
- CV including publication list,
- PhD certificate or confirmation of expected completion, and
- contact details of at least two academic references.
The position is available immediately.
You can find out more about our research at:
https://pharma.unibas.ch/de/research/research-groups/computational-pharmacy-2155/
For questions, please contact Prof. Markus Lill (Write an email).
Universität Basel
4000 Basel
4000 Basel
