The Computational Pharmacy group at the University of Basel invites applications for a fully funded Postdoctoral Position in AI-Driven Drug Design. This position is part of an international Innosuisse research project focused on building an integrated, closed-loop Design–Make–Test–Analyze (DMTA) platform that combines state-of-the-art machine learning, generative molecular design, and experimental validation.
Position Overview
| Designation | Postdoctoral Researcher / Postdoctoral Fellow |
|---|---|
| Research Area | AI-Driven Drug Design, Computational Chemistry, Machine Learning, Cheminformatics |
| Host Institution | University of Basel (Department of Pharmaceutical Sciences / Computational Pharmacy) |
| Location | Basel, Switzerland |
| Employment Type | Full-time (100%), Fully Funded |
| Starting Date | Available immediately |
| Application Deadline | Open until filled (Applications reviewed continuously) |
Research Project & Job Description
Artificial intelligence is transforming early drug discovery. However, identifying clinical candidates requires balancing target affinity with selectivity, physicochemical properties, synthetic accessibility, off-target liabilities, and iterative experimental feedback. This Innosuisse-backed research initiative aims to establish an autonomous, closed-loop DMTA platform utilizing generative AI, ultra-large chemical spaces, and physics-informed representations, demonstrated on serine protease targets from the complement system.
As a postdoctoral fellow in this project, your main responsibilities will include:
- Developing and fine-tuning machine learning and deep learning architectures for structure-based and generative molecular design.
- Embedding physicochemical knowledge and structural protein–ligand interaction profiles into generative workflows.
- Building automated workflows for closed-loop DMTA cycles where experimental affinity and selectivity data iteratively refine subsequent compound generations.
- Prospectively applying and validating algorithmic designs for the lead optimization of serine protease inhibitors.
- Collaborating closely with an interdisciplinary team of experimental chemists, biologists, and computational scientists from academic and industry partner institutions.
- Preparing research findings for publication in top-tier peer-reviewed journals and presenting at international conferences.
Eligibility and Qualifications
Applicants must meet the following criteria:
- Education: A completed PhD in Computational Chemistry, Cheminformatics, Computer Science, Physics, Biophysics, or a closely related quantitative field.
- Technical Skills: Strong foundation in modern machine learning and deep learning methodologies, accompanied by advanced programming proficiency in Python.
- Domain Expertise: Proven experience in one or more of the following:
- Molecular generative models (e.g., diffusion models, VAEs, transformers).
- Cheminformatics, molecular representations, and ultra-large virtual screening.
- Structure-based drug design, molecular dynamics, or protein–ligand docking.
- Desirable Knowledge: Familiarity with molecular modeling suites and the physical principles underlying molecular recognition.
- Publication Record: A demonstrated track record of scholarly publications in reputable peer-reviewed journals (e.g., JCTC, JCIM, JCP) or premier machine learning venues (e.g., NeurIPS, ICML, ICLR).
- Languages & Soft Skills: Full professional proficiency in written and spoken English, excellent teamwork capabilities, and high scientific curiosity.
What We Offer
- An opportunity to conduct impactful research at the cutting edge of artificial intelligence and molecular therapeutics.
- Direct integration of computational methodologies with wet-lab synthetic and biological assays in active prospective cycles.
- A vibrant, multicultural research environment in Basel—one of Europe’s leading hubs for pharmaceutical research and biotechnology.
- Close networking and collaboration with premier industrial and academic consortia partners.
How to Apply
Interested candidates should submit their application through the official recruitment portal of the University of Basel. Make sure to prepare the following documents in English (preferably as a single combined PDF):
- A detailed Curriculum Vitae (including a full list of publications).
- A cover letter explaining your research background, motivation, and fit for the position.
- Copies of university degree certificates and academic transcripts.
- Contact details of at least two academic referees.
Last Date for Apply
The position is available immediately. Applications will be evaluated on a rolling basis until the position is filled. Early submission is strongly encouraged.
Apply Link
To access the official application form and submit your application, please visit:
Apply Online – Universität Basel Postdoctoral Position in AI-Driven Drug Design
