Postdoctoral Position in AI-Driven Drug Design

University of Basel, Switzerland

Updated: 14 days ago

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:

  • 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.

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