AI-Driven Drug Design Postdoc - Fully Funded, Global Team

ScholarLink Inc.; Basel; Hybrid; CHF 90,000 - 110,000; Full time

Job summary

University of Basel invites applications for a fully funded Postdoctoral position in AI-driven drug design within the Computational Pharmacy group. The role focuses on developing and applying machine-learning and physics-informed methods in an integrated Design–Make–Test–Analyze (DMTA) platform for iterative drug discovery.

Collaboration spans international partners and industrial collaborators. The project centers on an AI-driven closed-loop workflow with a serine protease as a real-world lead

Qualifications

• PhD in a computational field relevant to drug design.
• Experience with structure-based drug design and generative models.
• Experience implementing closed-loop DMTA workflows and validating designs.

Responsibilities

• Develop and adapt machine-learning approaches for structure-based and generative molecular design.
• Integrate physicochemical information, including protein–ligand interaction features, into AI workflows.
• Develop computational workflows for DMTA cycles using experimental data to iteratively improve molecule design.
• Collaborate with chemists, biologists, and international project partners.
• Contribute to scientific publications and project reporting.

Skills

Machine learning

Structure-based design

Generative chemistry

Protein–ligand interactions

DMTA workflows

Education

PhD in Computational

Tools

Molecular modeling software

Job description

University of Basel invites applications for a fully funded Postdoctoral position in AI-driven drug design within the Computational Pharmacy group. The role focuses on developing and applying machine-learning and physics-informed methods in an integrated Design–Make–Test–Analyze (DMTA) platform for iterative drug discovery.

Collaboration spans international partners and industrial collaborators. The project centers on an AI-driven closed-loop workflow with a serine protease as a real-world lead