Successful candidates will develop and apply artificial intelligence/machine learning (AI/ML) approaches to identify known and unknown molecules within mass spectrometry data. They will have an opportunity to lead and contribute to a range of exciting projects: for example, translating tandem mass spectrometry data (MS/MS) into chemical structures to enable de novo annotation of unknown metabolites, or developing machine-learning workflows to preprocess LC-MS experiments. Opportunities are also available to support the development of an independent research agenda that is congruent with the interests and goals of the laboratory.

The scope of the work builds on recent publications from the laboratory, e.g. integrating language models with mass spectrometry data (https://www.nature.com/articles/s42256-021-00407-x, https://www.nature.com/articles/s42256-021-00368-1) developing bio- and cheminformatic tools to discover bacterial natural products (https://www.nature.com/articles/s41467-020-19986-1, https://www.nature.com/articles/nchembio.2018), or executing repository-scale meta-analyses of mass spectrometric datasets (https://www.nature.com/articles/s41592-021-01194-4). The research is entirely computational in nature, but requires interacting closely with experimental collaborators and their datasets. Many of the problems are constrained by inherently low-quality or noisy data, and candidates will therefore be expected to be engaged in data preprocessing and curation in addition to model development and evaluation.

This opportunity will prepare incumbent(s) for a range of competitive positions in academia or industry that involve computational biology/chemistry, machine-learning for biological 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, or machine learning/computer science.

Appointments are for one year with the possibility of renewal pending satisfactory performance and continued funding. Individuals should have or be expected to have a PhD with appropriate research experience in computational biology, chemistry, biochemistry, computer science, biological engineering, or a related field. To apply online, please visit https://www.princeton.edu/acad-positions/position/31201 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.