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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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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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. Engagement with government and community stakeholders and partners. Presentation of research at local, national and international conferences and meetings. Additional Information: Applicants must hold a PhD in
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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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. 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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, 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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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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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