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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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data-driven, computational approaches. Successful candidates will be willing and able to work across a breadth of disciplines - from genomics to computer science, sociology to psychology, engineering to
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codebases and data pipelines; ensure reproducibility and version control *Work with team members to integrate LLM modules into user friendly decision support platforms *Facilitate user testing and gather
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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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sample of writing in the candidate's field of specialization 4) contact information for three or more references Applications received by November 1, 2025 will be assured of full consideration. Expected
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materials:1) a cover letter of application2) a curriculum vitae3) a sample of writing in the candidate's field of specialization4) contact information for three or more references Applications received by
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closely-related field. Applicants should include a cover letter, a curriculum vitae including a publication list, and contact information for three references by applying on the Princeton University
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, including a list of publications and presentations, a summary of research accomplishments and interests, and the names and contact information of at least three potential references to https
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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