Position: Postdoctoral Research Associate - AI/Machine Learning for Mass Spectrometry

Description: The Skinnider Lab (https://ludwigcancer.princeton.edu/people/michael-skinnider) at Princeton University aims to recruit between 1 to 2 postdoctoral or more senior research positions to work on projects related to machine-learning for mass spectrometry-based metabolomics data. Positions are available starting July 2024, and will remain open until excellent fits are found. 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, developing machine-learning approaches for the identification of known and unknown metabolites in MS/MS data; meta-analysis of mass spectrometry-based metabolomics from human disease; re-analysis of large-scale clinical datasets to identify new illicit drugs; collaboration with anti-doping programs to identify novel performance-enhancing drug; or curation and development of new data resources. 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 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 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. 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, or a related field. To apply online, please visit https://puwebp.princeton.edu/AcadHire/position/35542and 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.

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