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and a strong commitment to excellence in education are encouraged to apply. PhD is expected by the start date. Applicants must apply online at https://puwebp.princeton.edu/AcadHire/position/38042 and
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to excellence in education are encouraged to apply. A PhD in Materials Science, Optics, Physics, Chemistry, Electrical, Chemical, Mechanical, Civil or Bio Engineering or related area is required. We
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on data science and engineering. The scientist will collaborate with Princeton and GFDL researchers to enhance, analyze and deliver high-resolution earth system model data, with an emphasis on Seamless
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health-related research problems using advanced data-driven, computational approaches. Successful candidates will be willing and able to work across a breadth of disciplines - from genomics
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fine tuning and RAG workflows for LLMs on a variety of datasets*Maintain codebases and data pipelines; ensure reproducibility and version control*Work with team members to integrate LLM modules into user
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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
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should submit a curriculum vitae, a publication list and a research statement, and provide contact information for three references by November 1, 2024 11:59 (EDT). Candidates may also include a cover
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leading and mentoring graduate and undergraduate students. A PhD in relevant fields of energy storage, electrochemistry, and materials characterization is required. Experience with solid electrolytes
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genomic data for reconstructing evolutionary patterns and processes that have shaped biological history across deep timescales. The ideal candidate will have a background in phylogenomics and bioinformatics
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contact information for three references. This position is subject to the University's background check policy. The work location for this position is in-person on campus at Princeton University.The Term