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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757
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The Ghanim lab is seeking a postdoctoral candidate or more senior researcher to study macromolecular complexes involved in LINE-1 retrotransposition using an integrated biochemical and structural
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: 275311206 Position: Postdoctoral Research Associate Description: The Ghanim lab is seeking a postdoctoral candidate or more senior researcher to study macromolecular complexes involved in LINE-1
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Rate Join our Talent Network to receive updates about working at Princeton. Princeton University job offers are contingent upon the candidate’s successful completion of a background check, reference
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social networks and their physical and social environment receive information and cues from those sources as well as feedback from the effect of their decisions. The BSPL puts a special emphasis on
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of laminar/neuropixel probes and electrical microstimulation to study attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH
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models, programming, and quantitative methods. Preferred qualifications include experience in reinforcement learning, neural networks, and/or statistics. Questions can be addressed to Professor Nathaniel
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of epistemic values in scientific practice, or the expression of values in collective behaviors (e.g., in online social networks). The proposed research is expected to yield both theoretical and empirical
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models, programming, and quantitative methods. Preferred qualifications include experience in reinforcement learning, neural networks, and/or statistics. Questions can be addressed to Professor Nathaniel
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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