Postdoctoral Researcher in Natural Product Research: The University of Geneva is offering a prestigious postdoctoral researcher position within the Bioactive Natural Products Unit. This role is part of the ANR-SNF funded project MetaboLinkAI, which focuses on leveraging artificial intelligence (AI), knowledge graphs (KG), and machine learning to accelerate natural product research and metabolomics. The successful candidate will develop innovative methods for characterizing the metabolome of chemodiverse plants and micro-organisms, contributing to the discovery of bioactive natural products.
Postdoctoral Researcher in AI-Accelerated Natural Product Research
Designation: Postdoctoral Researcher
| Category | Details |
|---|---|
| Scholarship Name | 2025 Scholarship in Natural Products |
| University | University of Geneva |
| School | School of Pharmaceutical Sciences |
| Unit | Bioactive Natural Products Unit |
| Project | MetaboLinkAI |
| Eligibility | PhD or equivalent experience in natural product chemistry, metabolomics, or related field |
| Key Responsibilities | Metabolomics data analysis, natural product isolation, AI solution deployment |
| Laboratory Expertise | Phytochemistry and Bioactive Natural Products |
| Application Deadline | Until the position is filled |
| Starting Date | April 1, 2025, or as mutually agreed |
| Application Method | Email CV, motivation letter, and referee contacts to Prof. Jean-Luc Wolfender |
| Location | Geneva, Switzerland |
Research Area: Natural Product Chemistry, Metabolomics, Cheminformatics, Bioinformatics, Computational Chemistry, Artificial Intelligence
Eligibility/Qualification:
- PhD or equivalent experience in natural product chemistry, metabolomics, cheminformatics, bioinformatics, computational chemistry, or a related field.
- Familiarity with metabolomics data analysis and interpretation.
- Proficiency in programming languages commonly used in metabolomics (e.g., R, Python).
- Rigorous and organized approach to methodological data management.
Job Description:
- Process and integrate data from LC-MS metabolite profiling into a Metabolomics Knowledge Graph (MS/KG).
- Collaborate on the targeted isolation of natural products (NPs) for proof-of-concept bioactive scaffold discovery.
- Assist in generating fully characterized extracts for benchmarking.
- Work closely with AI experts to deploy next-generation AI solutions in metabolomics.
How to Apply:
Interested candidates should submit their application, including:
- CV
- Motivation letter
- Contact details of two referees
via email to Prof. Jean-Luc Wolfender at Jean-Luc.Wolfender@unige.ch
Last Date for Apply:
Applications will be considered until the position is filled.
