Successful candidates will develop and apply computational approaches for mass spectrometry data, with artificial intelligence/machine learning (AI/ML) being a major focus. They will have an opportunity to lead and contribute to a range of exciting projects: for example, re-analysis of large-scale clinical datasets to identify new illicit drugs or collaboration with anti-doping programs to identify novel performance-enhancing drugs.

The scope of the work builds on recent publications from the laboratory, e.g., predicting future illicit drugs with chemical language models (https://www.nature.com/articles/s42256-021-00407-x) and re-analyzing large-scale clinical datasets for illicit drug identification (https://pubs.acs.org/doi/full/10.1021/acs.analchem.3c03451). The research is computational in nature but involves close interactions with experimental and clinical collaborators.

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

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.

Individuals should have or be expected to have a PhD with appropriate research experience in computational biology, chemistry, biochemistry, computer science, biological engineering, toxicology, or a related field. To apply online, please visit https://www.princeton.edu/acad-positions/position/33801 and 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.

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.