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and contact information for three references. The Term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory
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of design, computation, and robotics. ARG's research interests include topics such as robot learning, human-robot interaction, Generative AI, computer vision, closed-loop control, extended reality (XR), and
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, computation, and robotics. ARG's research interests include topics such as robot learning, human-robot interaction, Generative AI, computer vision, closed-loop control, extended reality (XR), and computational
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: 270175820 Position: Postdoctoral Research Associate Description: The Department of Electrical and Computer Engineering invites applications for postdoctoral, or more senior, research positions. The term
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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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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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, 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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. 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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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