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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation
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specific plans and goals for advancing equity and inclusion if hired as a Princeton postdoc, and contact information for three references. This position is subject to the University's background check policy
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they are likely to make to higher education in the future through teaching and writing about race. How To Apply: Applicants must upload to the online portal the following information by December 15, 2025, 11:59pm
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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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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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, 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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Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning, statistics, data science, applied math and/or other
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of phylogenomics to work with Professor Tiago Simões. The Simões lab is broadly interested in phylogenetic methods and applications, using morphological and genomic data for reconstructing evolutionary