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rank 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. Essential Qualifications
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computational chemistry. The Term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding
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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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contact information for three references. This position is subject to the University's background check policy. The work location for this position is in-person on campus at Princeton University. The Term
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are for one year with the possibility of renewal pending satisfactory performance and continued funding. This project is funded by the NSF award "Non-local magneto-curvature instabilities and their associated
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on rank. Positions at the postdoctoral rank 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
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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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of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. Applicants must apply online at https://puwebp.princeton.edu/AcadHire
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strong commitment to excellence in education are encouraged to apply. A Ph.D. is required. Postdoctoral appointments are for one year with the possibility of renewal pending satisfactory performance and
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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