Postdoctoral Position in AI-Driven Drug Design, University of Basel, Switzerland

Summary

The Computational Pharmacy group at the University of Basel is offering a fully funded Postdoctoral position within an international Innosuisse research project focused on AI-driven closed-loop drug discovery. The initiative aims to build an integrated Design–Make–Test–Analyze (DMTA) platform combining generative AI, ultra-large synthetically accessible chemical spaces, physics-informed molecular representations, and experimental feedback applied to prospective lead-optimization projects.

Designation

Postdoctoral Researcher / Postdoctorand (100%)

Key Job Overview

Research Area

• Artificial Intelligence & Machine Learning in Drug Design
• Computational Chemistry & Cheminformatics
• Generative Molecular Design & Protein–Ligand Interactions
• Closed-Loop DMTA (Design–Make–Test–Analyze) Workflows

Location

University of Basel, Basel, Switzerland

Eligibility & Qualifications

• Education: PhD in Computational Chemistry, Cheminformatics, Computer Science, Physics, or a related quantitative discipline (or expected completion in the near future).
• Technical Skills:
  • Strong background in machine learning and deep learning.
  • Proficient programming skills, especially in Python.
  • Demonstrated experience in at least one of the following:
    • Molecular generative AI
    • Cheminformatics and molecular representations
    • Structure-based drug design and protein–ligand modeling
  • Solid understanding of molecular modeling and the physicochemical principles governing molecular recognition is highly desirable.
• Publication Record: Proven track record with publications in high-quality, internationally recognized venues (e.g., top computational chemistry journals like JCTC, JCP, or premier machine learning conferences like NeurIPS, ICML, ICLR).
• Language & Soft Skills: Fluent written and spoken English; strong collaborative mindset, independence, and motivation to bridge methodological development with prospective drug discovery.

Job Description & Responsibilities

• Algorithm & Method Development: Develop and adapt machine learning architectures for structure-based and generative molecular design.
• Physics-Informed AI Integration: Integrate physicochemical descriptors and protein–ligand interaction features into generative workflows.
• Closed-Loop Workflow Implementation: Build computational pipelines where experimental affinity, selectivity, and molecular properties are continuously integrated into subsequent molecular generation rounds.
• Prospective Validation: Apply methods to prospective lead-optimization cycles focusing on serine protease inhibitors within the complement system.
• Interdisciplinary Collaboration: Work closely with an international consortium of computational scientists, medicinal chemists, and biologists.
• Dissemination: Present findings at international conferences, publish in leading scientific journals, and contribute to project reporting.

How to Apply

Interested candidates must submit their application dossier online via the University of Basel online recruiting platform containing:

1. Motivation Letter (max. 1 page) outlining research interests, relevant experience, and technical skill set.
2. Curriculum Vitae (CV) including a comprehensive publication list.
3. PhD Certificate (or official confirmation of expected completion).
4. References: Contact information for at least two academic referees.

• Contact for Inquiries: Prof. Markus Lill (markus.lill@unibas.ch)
• Application Link: Apply via University of Basel Job Portal

Last Date for Apply

• Available immediately (Applications are reviewed on a rolling basis until the position is filled).

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