Postdoc: AI-Driven Metabolomics & Unknown Metabolites

Ludwig Princeton Branch, Princeton University; Princeton (NJ); On-site; USD 70,000 - 90,000; Full time

Job summary

Princeton University invites applications for a postdoctoral fellow or more senior researcher to work on computational analysis of mass spectrometry datasets, focusing on AI/ML methods to discover unknown metabolites linked to phenotypes.

The successful candidate will develop and apply computational approaches to metabolomics data, validate predictions with experimentalists, and contribute to metabolite discovery campaigns related to human disease, genetics, and diet. In-person on campus.

Qualifications

• PhD with strong publication record in relevant fields.
• Experience with computational mass spectrometry is valuable.

Responsibilities

• Lead computational analyses of mass spectrometry-based metabolomics datasets.
• Develop and apply AI/ML tools to assign structures to unidentified peaks.
• Collaborate with experimentalists to validate predictions and drive wet-lab discoveries.
• Mentorjunior researchers and contribute to career development.

Skills

Computational biology

Bioinformatics

Metabolomics

Machine learning

Mass spectrometry

Cheminformatics

Written communication

Independent work

Education

PhD in CS/Computational biology/Chemistry/Biochemistry

Tools

Mass spectrometry software

Job description

Princeton University invites applications for a postdoctoral fellow or more senior researcher to work on computational analysis of mass spectrometry datasets, focusing on AI/ML methods to discover unknown metabolites linked to phenotypes.

The successful candidate will develop and apply computational approaches to metabolomics data, validate predictions with experimentalists, and contribute to metabolite discovery campaigns related to human disease, genetics, and diet. In-person on campus.