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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
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who are unable to upload unofficial transcripts may send official transcripts to Politics Postdoc Search, Department of Politics, 001 Fisher Hall, Princeton University, Princeton, NJ 08540. A PhD is
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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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the applicant: *Cover letter *Curriculum vitae *Transcripts *Research Proposal indicating plans for two-year postdoc (maximum 5 pages double-spaced) *Dissertation abstract (including Table of Contents) *Writing
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be sent to amferris@princeton.edu with the subject line "Ferris Lab Postdoc Inquiry 2025". Applications will be reviewed on a rolling basis, until the position is filled, with a final deadline of
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://puwebp.princeton.edu/AcadHire/position/36402 and submit a cover letter, CV, a research statement that includes your specific plans and goals for advancing equity and inclusion if hired as a Princeton postdoc, and
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"Ferris Lab Postdoc Inquiry 2025". Applications will be reviewed on a rolling basis, until the position is filled. Expected Salary Range: 65000-70000 The University considers factors such as (but not
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the ability to controllably engineer diamond surfaces to enable future advanced applications of diamond electronic and quantum devices and sensors. Fundamental studies using highly controlled exposures
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of laminar/neuropixel probes and electrical microstimulation to study attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH
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models, programming, and quantitative methods. Preferred qualifications include experience in reinforcement learning, neural networks, and/or statistics. Questions can be addressed to Professor Nathaniel