Postdoctoral Research Associate - Computational Mass Spectrometry

Princeton University - Princeton, NJ

Full-time - Entry Level

Princeton, NJ

Educational Services

About the position

The Skinnider Lab of the Ludwig Princeton Branch at Princeton University is seeking to recruit one to two postdoctoral associates or more senior research positions focused on computational analysis and machine-learning approaches to mass spectrometry-based metabolomics and/or proteomics data. The positions are set to commence in March 2024 and will remain open until suitable candidates are found. The successful candidates will be responsible for developing and applying computational methods for mass spectrometry data, with a significant emphasis on artificial intelligence and machine learning (AI/ML). Candidates will have the opportunity to lead and contribute to a variety of innovative projects, such as creating machine-learning techniques for identifying both known and unknown metabolites in MS/MS data, conducting meta-analyses of mass spectrometry-based metabolomics related to human diseases, and developing novel methodologies for proteomic data analysis. Additionally, there will be opportunities to curate and develop new data resources. The work will build upon recent publications from the lab, which include integrating language models with mass spectrometry data and developing bio- and cheminformatic tools for discovering bacterial natural products. The research is primarily computational but requires close collaboration with experimental partners. Many challenges will involve dealing with low-quality or noisy data, and the ideal candidate will be enthusiastic about contributing to data preprocessing and curation alongside model development and evaluation. This position is designed to prepare candidates for competitive roles in academia or industry that focus on computational biology/chemistry, machine learning for biological data, and drug discovery/design. Mentorship is a key component of this role, and every effort will be made to support the candidate in achieving their career goals. The successful candidate will be motivated, independent, and possess strong written communication skills.

Responsibilities

• Develop and apply computational approaches for mass spectrometry data.
• Lead and contribute to projects involving machine-learning techniques for metabolite identification.
• Conduct meta-analyses of mass spectrometry-based metabolomics data related to human diseases.
• Develop novel methodologies for proteomic data analysis.
• Curate and develop new data resources.
• Collaborate closely with experimental partners to address research challenges.
• Contribute to data preprocessing and curation in addition to model development and evaluation.
• Support the development of an independent research agenda aligned with laboratory goals.

Requirements

• PhD in computational biology, chemistry, biochemistry, computer science, biological engineering, or a related field.
• Experience in computational biology/bioinformatics, cheminformatics, analytical chemistry/mass spectrometry/metabolomics, or machine learning/computer science, demonstrated through at least one first-author publication.
• Strong written communication skills.
• Motivated and independent work ethic.

Nice-to-haves

• Experience with artificial intelligence and machine learning techniques.
• Familiarity with mass spectrometry data analysis.
• Background in drug discovery or design.

Benefits

• Mentorship opportunities to support career development.
• Access to cutting-edge research projects.
• Collaboration with experimental researchers.