The Skinnider Lab at Princeton University aims to recruit a postdoctoral fellow or more senior research position to work on projects related to computational analysis of mass spectrometric datasets. A major focus will be on the application of AI/machine learning models and other computational methods to discover unknown metabolites that have strong associations to experimental variables or phenotypes, but which have eluded identification. The position will remain open until excellent fits are found.

The successful candidate will develop and apply computational approaches to mass spectrometry-based metabolomics datasets, with discovery of unknown metabolites being a major focus. The laboratory is engaged in several metabolite discovery campaigns involving mining of large-scale metabolomic datasets to discover novel metabolites associated with human disease, genetics, and dietary exposures. The candidate will lead computational analyses of these datasets, using the laboratory’s suite of existing AI/ML tools to assign structures to unidentified peaks in metabolomic datasets (e.g., https://www.nature.com/articles/s42256-021-00407-x), and extending these tools or developing new models as needed. The candidate will have the opportunity to work directly with experimentalists to validate predictions and drive wet-lab discoveries, or to perform experiments themselves if they wish.

This opportunity will prepare candidates for a range of competitive positions in academia or industry that involve computational biology/chemistry, machine-learning for biological or chemical data, metabolism, and drug discovery/design. Mentorship is taken seriously and every effort will be made to ensure the candidate is able to achieve goals in the next stage of their career.

The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. The work location for this position is in-person on campus at Princeton University.

This position is subject to Princeton University's background check policy.

The successful candidate will be motivated, independent, and have strong written communication skills. Candidates are required to have experience in one or more of the following areas as demonstrated through at least one first-author publication: computational biology/bioinformatics, metabolism/metabolomics, analytical chemistry/mass spectrometry, cheminformatics, or machine learning/computer science. Prior experience in computational mass spectrometry will be viewed favorably but is not a requirement if the candidate has complementary expertise.

Individuals should have or be expected to have a PhD with appropriate research experience in computer science, computational biology, biological or chemical engineering, chemistry, biochemistry, or a related field. Qualified candidates who pass an initial screening may be provided with short programming exercises to assess their skills.

To apply online, please submit CV and cover letter. Cover letter should highlight 1-3 publications or preprints that you feel best address the requirement for experience in above-mentioned areas. Please also include contact information for three references.