SNSF-funded scientific research position in chemometrics or computational biology

About the Project

Dysregulation of lipid metabolism is pivotal in the development and progression of metabolic disorders such as obesity, type 2 diabetes, and metabolic dysfunction-associated steatotic liver disease (MASLD). This initiative is fully funded by the Swiss National Science Foundation (SNSF) and aims to advance research in this critical area.

Expectations

The core objective of this role involves enhancing the identification and screening of novel small molecules, with a specific emphasis on understanding the structure and function of emerging lipids associated with metabolic disorders.

Key Responsibilities

• Develop and apply computational pipelines for the identification and annotation of novel lipids.
• Conduct in silico structural predictions of novel or low-abundance lipid species using sophisticated cheminformatics tools.
• Integrate multi-omics data with metabolic modeling approaches to explore lipid flux and changes in metabolic pathways.
• Design and implement chemometric and machine learning models for patient stratification based on spectral and imaging data.

Candidate Profile

The preferred candidate will possess a PhD or Master's degree with a minimum of 5 years of research experience in computational biology, systems biology, bioinformatics, or chemometrics. Proficiency in at least one programming language (R, MATLAB, Python) is required. A demonstrated history in mass spectrometry spectral analysis or imaging analysis, as well as in silico chemical annotation and machine learning methods, is essential. Familiarity with metabolic processes is considered advantageous. We seek a passionate individual capable of independent work as well as collaboration within a team, equipped with excellent communication skills and fluency in both spoken and written English.

What We Provide

Our organization offers a supportive, state-of-the-art research environment designed for scientific and professional growth. The successful candidate will receive training as part of a multidisciplinary research project and will engage with extensive datasets generated from both pre-clinical and clinical studies. This opportunity is available from September 2025 or upon mutual agreement.