89 algorithm-development-"Multiple" "Prof" Postdoctoral positions at Princeton University
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on social vulnerability to hazards. The researcher will have the opportunity to work on multiple projects, investigating (a) cumulative environmental impacts, (b) the use of census microdata for social
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-frequency comb laser for use in conjunction with a unique tip-enhanced spectroscopy instrument, currently under development in Prof. Rabitz's lab, to produce the world's first nanoscale dual-frequency comb
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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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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral or more senior research scientist to develop hybrid models for sea ice that combine coupled climate models and machine
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The group of Prof. Aditya Sood in the Department of Mechanical and Aerospace Engineering and the Princeton Materials Institute at Princeton University invites applications for postdoctoral positions
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and machine learning with Prof. Jason M. Klusowski (https://klusowski.princeton.edu). The position is for one year with the possibility of reappointment based on satisfactory performance and
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experimental research related to multiple ongoing projects, including optical diagnostic design and high-temperature ammonia oxidation chemistry with applications to green manufacturing and recycling of steel
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background in human or monkey electrophysiology. Studies will include simultaneous recordings and stimulation from multiple, interconnected brain regions. The researcher will gain experience with the use
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: 272540364 Position: Postdoctoral Research Associate Description: The condensed matter spectroscopy group at Princeton University invites applications for multiple Postdoctoral Research or more senior
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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