88 component-labeling-agorithm-cuda Postdoctoral research jobs at Princeton University
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, and organizational considerations when extending an offer. The posted salary range represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time
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range represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly. The University also offers a comprehensive
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organizational considerations when extending an offer. The posted salary range represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro
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position; salaries for part-time positions are pro-rated accordingly. The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information.
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an offer. The posted salary range represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly. The University also
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ability to work as part of a team and to mentor graduate students is desired. The candidate is expected to have good management skills relevant to a multi-user environment, good communication and
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, construction, operation, and maintenance of UHV instruments and chambers, including those for PVD, is of interest. A demonstrated ability to work as part of a team and to mentor graduate students is desired
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chambers, including those for PVD, PLD, and RIE, is of interest. A demonstrated ability to work as part of a team and to mentor graduate and undergraduate students is desired. The candidate is expected
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Through the department of Physics at Princeton University, Openings are available for a postdoctoral research associate as part of the Gordon and Betty Moore Foundation's Emergent Phenomena in
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
coupled climate model simulations. The project will involve: 1) the development of a neural network that conserves heat, mass, and salt across model components; 2) implementation of the network in the SIS2