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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 delivery of clean energy and industrial decarbonization infrastructure associated with net-zero transitions. The role will report to the Andlinger Center's Dr. Chris Greig, the Theodora D. '78 and William H
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/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 Silvio O
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experimentalists, materials synthesis experts and theorists to maximize their potential to explore, discover and understand emergent behavior of complex quantum matter. The Moore Postdoctoral Scholars in Theory
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environmental studies - to make novel insights and to tackle the full complexity of human health. We seek applicants to join any of the several interdisciplinary research projects within Princeton Precision
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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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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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: 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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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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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