147 phd-position-wireless-sensor-networks Postdoctoral positions at Princeton University
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ability to independently lead research projects. Candidates must also be comfortable working with and mentoring graduate and undergraduate student researchers. To be eligible for this position, a PhD in
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: 273379270 Position: Postdoctoral Research Associate Description: The group of Prof. Aditya Sood in the Department of Mechanical and Aerospace Engineering and the Princeton Materials Institute at Princeton
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The Department of Psychology at Princeton University invites applications for a Postdoctoral Research Associate or more senior research position. Applicants should have a PhD degree (or expect
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. A major focus will be on the identification of small molecules from mass spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available
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must also be comfortable working with and mentoring graduate and undergraduate student researchers. To be eligible for this position, a PhD in Mechanical Engineering, Aerospace Engineering, Chemical
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The Koel laboratory in the Department of Chemical Engineering at Princeton University is seeking a postdoctoral or more senior researcher position to work on a new collaborative project in a team
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Princeton University, Skinnider Lab Position ID: Princeton University -Skinnider Lab -PDRA [#30158] Position Title: Position Location: Princeton, New Jersey 08544, United States of America [map
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leverage these findings for bioengineering applications. Candidates completing (i.e., with a confirmed defense/viva date) or holding a PhD in chemical engineering, physics, bioengineering, chemistry, or a
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availability of funding. The anticipated start date for the position is June 1, 2025. Individuals with a strong theoretical background who expect to obtain a PhD in a related field (e.g., statistics
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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