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access to state-of-the-art numerical models and high-performance computing systems at Princeton and in NOAA, working alongside GFDL model developers and software engineers to advance quality assurance and
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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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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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expects to have post-doctoral or more senior research positions in Theoretical High-Energy Physics, broadly defined, starting around September 1, 2025. The applicants should have a Ph.D. in Physics and
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the Department of Chemical and Biological Engineering to study the biochemical and mechanical mechanisms that define pattern formation during branching morphogenesis of the lung and mammary gland. Further
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of ecology, evolution, and/or behavior, while also contributing to Princeton University's excellence and diversity, broadly defined. The Program is intended to foster the onward scientific careers
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Python programming skills are required, and prior experience with developing open-source software or databases will be considered a plus. Candidates should apply at: https://puwebp.princeton.edu/AcadHire
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access to state-of-the-art numerical models and high-performance computing systems at Princeton and in NOAA, working alongside GFDL model developers and software engineers to advance quality assurance and
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the biochemical and mechanical mechanisms that define pattern formation during branching morphogenesis of the lung and mammary gland. Further information about the lab can be found at www.princeton.edu