Fully Funded Postdoc: AI-Driven Drug Design & DMTA

Karlstad University; Basel; Vor Ort; CHF 90.000 - 120.000; Vollzeit

Zusammenfassung

The University of Basel invites applications for a fully funded Postdoctoral position in AI-driven drug design within the Computational Pharmacy group. The project aims to combine generative AI, large synthetic spaces, physics-informed representations, and experimental feedback in closed-loop DMTA cycles.

The successful candidate will develop and validate computational AI methods, collaborate with international industrial partners, and contribute to publications and project reporting.

Qualifikationen

• 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 molecular generative AI, cheminformatics, and structure-based drug design.
• A strong publication record in leading venues such as JCTC, Journal of Chemical Physics, or top ML conferences.
• Fluent verbal and written English.

Aufgaben

• Develop machine-learning approaches for structure-based and generative molecular design.
• Integrate physicochemical information into generative AI workflows.
• Develop computational workflows for closed-loop DMTA cycles using experimental data to improve designs.
• Apply and validate approaches in design and optimization of serine protease inhibitors.
• Collaborate with computational scientists, chemists, and biologists within the international project consortium.
• Contribute to publications, presentations, and project reporting.

Kenntnisse

Machine learning

Deep learning

Python programming

English communication

Ausbildung

PhD in Computational Chemistry / Cheminformatics / Computer Science / Physics or related

Tools

Python

Jobbeschreibung

The University of Basel invites applications for a fully funded Postdoctoral position in AI-driven drug design within the Computational Pharmacy group. The project aims to combine generative AI, large synthetic spaces, physics-informed representations, and experimental feedback in closed-loop DMTA cycles.

The successful candidate will develop and validate computational AI methods, collaborate with international industrial partners, and contribute to publications and project reporting.