Postdoctoral Position In Ai-driven Drug Design

# Postdoctoral Position in AI-Driven Drug Design Postdoc position
· Post Doctoral
· Switzerland Position type:
Postdoc postdoctoral

Level:
Post Doctoral Language:
English## About the Position 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:(Use the "Apply for this Job" box below)..
2c01436.
1c01438 positionA 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.

Your profile

PhD in Computational## Other Requirements & Additional Info

Employment type:

FULL _TIME