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Field
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Meteorological Laboratory (AOML). The incumbent is expected to: (1) Contribute to improvements in the data assimilation system configuration; (2) Perform ocean and coupled Observing System Experiments (OSEs
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machine learning. This part of the research at Princeton University/GFDL will involve working with the SPEAR ocean data assimilation system and the MOM6 ocean circulation model. The prognostic
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position. This position offers a unique opportunity for a highly motivated and talented researcher to contribute to cutting-edge research in the field of coupled data assimilation. The successful candidate
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machine learning. This part of the research at Princeton University/GFDL will involve working with the SPEAR ocean data assimilation system and the MOM6 ocean circulation model. The prognostic
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will involve working with the SPEAR ocean data assimilation system and the MOM6 ocean circulation model. The prognostic parameterizations will be state-dependent and trained to minimize model-observation
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are seeking an individual to work on 1) developing isotope-enabled Terrestrial Ecosystem Modeling, leveraging isotopic measurement data and exploring its potential applications in agroecosystems; 2) modeling
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(including figures and tables) and publish in peer-reviewed literature. Read literature articles, develop new ideas, and assimilate the information into his/her project design/interpretation. Dissemination
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publish in peer-reviewed literature. Read literature articles, develop new ideas, and assimilate the information into his/her project design/interpretation. Dissemination of studies: Interact on a regular
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data assimilation Advanced analytics of vehicle components/systems and ecosystems/operations/energy optimizations in freight mobility systems or networks providing recommendations on when/where
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experience is required, along with strong skills in statistical data analysis and scientific programming (Python, R, Matlab, or similar). Experience with active microwave remote sensing, GIS, data assimilation