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://www.princeton.edu/acad-positions/position/38722 and submit a cover letter, CV, and contact information for three references. This position is subject to the University's background check policy. The work location for
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advance regenerative medicine. For more information about the lab, please visit https://mesa-lab.org/. Projects will utilize in vivo mouse models, transcriptomic techniques, and advanced intravital imaging
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on data science and engineering. The scientist will collaborate with Princeton and GFDL researchers to enhance, analyze and deliver high-resolution earth system model data, with an emphasis on Seamless
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discussion of past research and expertise) and contact information for three references. This position is subject to the University's background check policy. The work location for this position is in
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one-page statement of research experience and interests, and a cover letter that includes names and contact information of three references. Princeton University is an Equal Opportunity Employer and
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appointments. Interested applicants must apply online at https://puwebp.princeton.edu/AcadHire/position/35482 and submit a cover letter, curriculum vitae, contact information for three references and a brief
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. Applicants should upload: -a cover letter -statement of research -teaching background, interests, and philosophy -curriculum vitae -contact information for three references as part of the application process
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, research statement, and contact information for 3 references). The work location for this position is in-person on campus at Princeton University. This position is subject to the University's background
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with measurement electronics for data acquisition, etc. Experience in the following areas is beneficial but not required: nonlinear optics (e.g. optical parametric amplifiers), electrical switching, high
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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