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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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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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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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. Applications should include a CV, a cover letter, a brief statement of research experience and research they hope to undertake at Princeton, and the contact information for three references. The application
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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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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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research and to their own work. Eligible candidate must have less than five years of post-PhD research experience prior to anticipated start date. This is a one-year term position ideally starting September
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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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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757