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. Some experience with first-principle methods (FP/DFT) and/or other forms of electronic and magnetic structure theory and calculations is also expected. The successful candidate will have a strong
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. Some experience with first-principle methods (FP/DFT) and/or other forms of electronic and magnetic structure theory and calculations is also expected. The successful candidate will have a strong
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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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superconductors. The successful candidate must have substantial experience in state-of-the-art ARPES and/or low temperature STM/STS techniques. Some experience with first-principle methods (FP/DFT) and/or other
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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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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
models, and their coupling, using machine learning. The postdoc will be expected to collaborate with other postdocs at Princeton and with other members of the M2LInES project across multiple institutions
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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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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
and atmosphere components of existing coarse resolution IPCC-class climate models, and their coupling, using machine learning. The postdoc will be expected to collaborate with other postdocs