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- Interfacing machine learning with climate models Company: Princeton University Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid
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conduct research on the use of machine learning in ocean climate models. The goal is to demonstrate the successful use of machine learned parameterizations of unresolved processes that will reduce biases in
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conduct research on developing and using machine learned parameterizations for mixing in the ocean surface boundary layer. Our previous work has demonstrated the utility of using neural networks to improve
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approaches for mass spectrometry data, with artificial intelligence/machine learning (AI/ML) being a major focus. They will have an opportunity to lead and contribute to a range of exciting projects
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conduct research on developing and using machine learned parameterizations for mixing in the ocean surface boundary layer. Our previous work has demonstrated the utility of using neural networks to improve
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on projects related to machine-learning for mass spectrometry-based metabolomics data. Positions are available starting September 2023, and will remain open until excellent fits are found. Successful candidates
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The Center for Statistics and Machine Learning (CSML) at Princeton University invites applications for Postdoctoral Research Associates position. Appointments are for one year with the possibility
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Company: Princeton University Description: The Center for Statistics and Machine Learning (CSML) at Princeton University invites applications for Postdoctoral Research Associates position. Appointments
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towards robustness and exploration. Experience with probability/statistics, calculus, machine learning, reinforcement learning. Be advised that you will be contacted only if there is further interest in
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) - Willingness to learn new skills and enthusiastically take on new projects and responsibilities - Attention to detail, high level of self-motivation, and ability to work independently - Strong interpersonal and