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, full-time Postdoctoral position within an international Innosuisse research project focused on AI-driven closed-loop drug discovery. The project aims to develop and implement an integrated Design–Make–Test–Analyze (DMTA) platform combining generative AI, physics-informed molecular representations, ultra-large synthetically accessible chemical spaces, off-target prediction, and experimental feedback. The methodologies will be prospectively validated on lead-optimization case studies targeting serine proteases in the complement system.

Designation

Postdoctoral Researcher (Postdoc) — 100% Workload

Overview & Key Details

Research Area

• Artificial Intelligence & Deep Learning in Drug Design
• Generative Molecular AI & Structure-Based Drug Design (SBDD)
• Cheminformatics & Molecular Representations
• Physics-Informed Machine Learning & Molecular Recognition
• Closed-Loop Design–Make–Test–Analyze (DMTA) Workflows

Location

University of Basel, Basel, Switzerland

Eligibility & Qualifications

• Education: Ph.D. in Computational Chemistry, Cheminformatics, Computer Science, Physics, or a closely related discipline (completed or confirmation of expected completion).
• Technical & Machine Learning Skills:
  • Strong background in machine learning and deep learning methodologies.
  • Proficiency in programming, especially Python.
  • Proven experience in at least one of:
    • Molecular generative AI
    • Cheminformatics and molecular representations
    • Structure-based drug design and protein–ligand modeling
• Domain Knowledge: Experience with molecular modeling and a strong understanding of physicochemical principles governing molecular recognition.
• Publication Track Record: A strong publication record in internationally recognized venues (e.g., top-tier ML conferences such as NeurIPS, ICML, ICLR, or leading computational chemistry journals such as JCTC, J. Chem. Phys., JCIM).
• Soft Skills & Language: Fluent written and oral communication skills in English; highly motivated, collaborative mindset, and ability to work at the intersection of method development and prospective drug discovery.

Job Description & Responsibilities

• Algorithm & Model Development: Develop and adapt machine learning architectures for structure-based and generative molecular design.
• Feature Integration: Incorporate physicochemical information and protein–ligand interaction features into generative workflows.
• Closed-Loop Platform: Construct computational workflows for closed-loop DMTA cycles where experimental affinity, selectivity, and property data iteratively guide molecular optimization.
• Prospective Validation: Apply and validate computational methodologies on real-world lead optimization projects (e.g., complement system serine protease inhibitors).
• Consortium Collaboration: Work closely with an international, multidisciplinary consortium of computational scientists, medicinal chemists, and biologists.
• Dissemination: Present research findings at international conferences, publish in high-impact peer-reviewed journals, and assist in project reporting.

How to Apply

Interested candidates should apply online via the University of Basel Online Recruiting Platform and submit the following documents in English:

1. Cover / Motivation Letter (maximum 1 page) detailing research interests, relevant experience, and qualifications.
2. Curriculum Vitae (CV) including a comprehensive publication list.
3. Ph.D. Certificate (or an official letter confirming expected graduation date).
4. References: Contact information for at least two academic referees.

• Application Portal: University of Basel Job Application Portal
• Informal Inquiries: Contact Prof. Markus Lill at markus.lill@unibas.ch
• Group Website: Computational Pharmacy Group

Last Date to Apply

Available immediately / Open until filled. Applications will be evaluated on a rolling basis, so prospective candidates are strongly encouraged to apply as early as possible.

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