151 optimization-nonlinear-functions Postdoctoral positions at Princeton University in United-States
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simultaneous recordings and stimulation from multiple, interconnected brain regions. The researcher will gain experience with the use of laminar/neuropixel probes and electrical microstimulation to study
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performance and continued funding; those hired at more senior ranks may have multi-year appointments. These positions are subject to the University's background check policy. The work location for this position
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satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. Essential Qualifications: PhD in a relevant discipline. Interested applicants must apply online
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of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. Start date is around September 1, 2026. The work location for this position
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are for one year with the possibility of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. Please include a cover letter, CV and
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The condensed matter spectroscopy group at Princeton University invites applications for multiple Postdoctoral Research or more senior positions to work in experimental condensed matter physics with
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, with a possibility for renewal contingent upon satisfactory performance and available funding. Applicants must have a Ph.D. in Geosciences or a closely-related field. Applicants should include a cover
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pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. These positions are subject to the University's background check policy. The work
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performance and continued funding; those hired at more senior ranks may have multi-year appointments. These positions are subject to the University's background check policy. The work location for this position
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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