2025 Postdoctoral Research Associate - AI/machine learning for analytical and forensic chemistry
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About The Position
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 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.
