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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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Values. The teaching will likely involve running a senior thesis seminar rather than teaching a traditional course, subject to approval of Princeton's Office of the Dean of the Faculty. The postdoc will
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required) Kinetic modeling proficiency (Chemkin, Cantera), analytical proficiency (sensitivity, rate of production, etc.) Spectroscopic modeling experience preferred (HITRAN/HITEMP) Familiarity with
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background in chemical and biological engineering, bio-engineering, molecular biology, microbiology, biochemistry, biophysics, computational modeling or related fields. Experience in metabolic engineering
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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approval of Princeton's Office of the Dean of the Faculty. The postdoc will be expected to participate in a year-long research seminar for visiting fellows and Center faculty, as well as a year-long seminar
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modeling. Positions may begin as soon as September 2025 . A Ph.D. in Mechanical and Aerospace Engineering, Civil and Environmental Engineering, Atmospheric and Oceanic Sciences, Geosciences, Computational
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-fabrication and knowledge in molecular biology, diagnostics, and/or optical measurements is a plus.2. Nanofabrication and applications. Candidates should have significant experience in micro/nano-patterning
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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
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continued funding; those hired at more senior ranks may have multi-year appointments. The ideal applicant will be highly motivated; good communication skills; have (or expect to have) a PhD in Molecular