The Skinnider Lab at Princeton University aims to recruit a postdoctoral fellow or more senior research position to work on projects related to the development of AI/machine learning approaches for chemical and biochemical datasets. A major focus will be on introducing new AI models to chart the chemical âœdark matter❠of the mammalian metabolome; examples of such models include large supervised or self-supervised AI models for mass spectrometry data, or multimodal models integrating multiple sources of analytical data for the discovery of metabolites and other types of small molecules. The position will remain open until an excellent fit is found.

The successful candidate will develop and apply computational approaches to biochemical datasets, with artificial intelligence/machine learning (AI/ML) being a major focus. Many of the laboratory's interests center around the discovery of previously unknown small molecules (metabolites) using mass spectrometry data. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models and drive wet-lab discoveries. The candidate may also have opportunities to work with research software engineers to translate their research into user-friendly software tools that will be used by a broad community.

The scope of the work builds on recent publications from the laboratory introducing new approaches or datasets for metabolite discovery from small molecule mass spectrometry (e.g., https://www.nature.com/articles/s41586-025-09969-x, https://www.nature.com/articles/s42256-021-00407-x, https://www.nature.com/articles/s42256-024-00821-x, https://pubs.acs.org/doi/10.1021/acs.analchem.5c06256, https://www.nature.com/articles/s41570-023-00570-2). The research is computational in nature but involves close interactions with experimental collaborators. Many of the problems are constrained by scattered or noisy data, and the successful candidate will be enthusiastic about contributing to data preprocessing and curation in addition to model development and evaluation.

This opportunity will prepare candidates for a range of competitive positions in academia or industry that involve machine-learning for biological or chemical data, computational biology/chemistry, or drug discovery/design, among others. 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.

This position is subject to Princeton University's background check policy. The work location for this position is in-person on campus at Princeton University.

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: machine learning/computer science, computational biology/bioinformatics, cheminformatics.

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.

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. Qualified candidates who pass an initial screening may be provided with short programming exercises to assess their skills. Only suitable candidates will be contacted.