Postdoctoral Research Associate - AI​/machine learning analytical and forensic chemistry

Position

2025 Postdoctoral Research Associate - AI/machine learning for analytical and forensic chemistry

The Skinnider Lab at Princeton University aims to recruit a postdoctoral fellow or more senior researcher to work on projects related to computational analysis of chemical and biochemical datasets. A major focus will be on the identification of small molecules from mass spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available starting July 2025, and will remain open until excellent fits are found.

The successful candidate will develop and apply computational approaches to chemical datasets, with artificial intelligence/machine learning (AI/ML) being a major focus. Many of the laboratory's interests center around the identification of small molecules using mass spectrometry data, and the use of language models to predict the existence of undiscovered small molecules that are likely to be observed by mass spectrometry. Of particular interest for this position is the identification of emerging illicit drugs, also known as novel psychoactive substances, in seized drug products or clinical samples.

The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models, and to develop user‑friendly tools that will be used by a broad community. The scope of the work builds on recent publications from the laboratory, e.g., integrating language models with mass spectrometry data (, https://(Use the "Apply for this Job" box below). , ) or executing large‑scale meta‑analyses of mass spectrometric datasets ().

The research is computational in nature but involves close interactions with experimental collaborators. Many of the problems are constrained by inherently low‑quality 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 computational biology/chemistry, machine‑learning for biological or chemical data, 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 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
• cheminformatics
• analytical chemistry/mass spectrometry/metabolomics
• machine learning/computer science

Individuals should have or be expected to have a PhD with appropriate research experience in computational biology, chemistry, biochemistry, computer science, biological or chemical engineering, forensic science, or a related field. Terms of appointment are 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.

To apply online, please visit https:// and submit a CV and cover letter. The cover letter should highlight 1-3 publications or preprints that you feel best address the requirement for experience in the 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.

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.

Expected Salary Range: $65,000 - $70,000

The University considers factors such as (but not limited to) scope and responsibilities of the position, candidate's qualifications, work experience, education/training, key skills, market, collective bargaining agreements as applicable, and organizational considerations when extending an offer. The posted salary range represents the University's good faith and reasonable estimate for a full‑time position; salaries for part‑time positions are pro‑rated accordingly.

The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information.

Requisition No: D-25-LPB-00006

PI