154 machine-learning-phd-in-netherland Postdoctoral positions at Princeton University
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funding.Applicants should have a strong track record of research excellence in a related field, and should have or be approaching a PhD in a related discipline (Physics, EE, Chemistry, or Computer Science
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: 278255392 Position: Postdoctoral Research Associate in Microfluidics, Nanofabrication, and Nanophotonics Description: The Department of Electrical and Computer Engineering has opening for postdoctoral
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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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August 2026. The Center supports empirical research on democratic political processes and institutions. PhD required. Each post-doctoral associate will pursue research and contribute to the intellectual
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science, electrical and computer engineering, sociology, public policy, information science, communication, economics, political science, psychology, philosophy, and related technology disciplines. Selected candidates
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experience in scholarly research and a strong commitment to excellence in education are encouraged to apply. PhD is required. Applicants must apply online at https://www.princeton.edu/acad-positions/position
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experience in scholarly research and a strong commitment to excellence in education are encouraged to apply. A PhD in Materials Science, Optics, Physics, Chemistry, Electrical, Chemical, Mechanical, Civil
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. Individuals with evidence of experience in scholarly research and a strong commitment to excellence in education are encouraged to apply. PhD is expected by the start date. Applicants must apply online
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The Department of Psychology at Princeton University invites applications for a Postdoctoral Research Associate. Applicants should have a PhD degree (or expect to receive a PhD degree by June 15
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials