AI/machine learning for performance-enhancing drug identification at Princeton University in Princeton, New Jersey

Job Description:

Position: AI/machine learning for performance-enhancing drug identification

Description: The Skinnider Lab at Princeton University aims to recruit a postdoctoral or more senior research position to work on projects related to the identification of performance-enhancing drugs via computational mass spectrometry. The position is available starting November 2024 and will remain open until excellent fits are found. The successful candidate 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 develop new AI/ML approaches for anti-doping, with a focus on identification of novel performance-enhancing drugs (PEDs). These so-called 'designer' PEDs include numerous high-profile examples of synthetic drugs that have allowed elite athletes to evade anti-doping programs in the past, such as tetrahydrogestrinone. The project seeks to accelerate the discovery and response to the proliferation of new doping agents through the development of chemical AI technologies. The successful candidate will develop the technologies in question, implement them in user-friendly software tools, and collaborate closely with anti-doping programs to apply these technologies for PED discovery. 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 the incumbent for a range of competitive positions in academia or industry that involve computational biology/chemistry, machine-learning for biological data, drug discovery/design, toxicology, sports science, 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. Applicants 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, sports science/anti-doping. 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, sports science, or a related field. To apply online, please visit https://puwebp.princeton.edu/AcadHire/position/37041 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 is an in-person position on Princeton University's campus and is subject to a background check.

PI254776364

Salary:

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