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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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postdoctoral associate will work on elements of the design, development and evaluation of a prototype East Coast Community Ocean Forecast System (ECCOFS) that implements data-assimilation of real-time
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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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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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Postdoctoral researcher : · Experience with data assimilation and/or inverse methods in geosciences. · Experience with computationally demanding models (e.g. climate, atmosphere or ocean). · Experience in
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to conduct research on developing and using machine learned parameterizations developed from ocean-data assimilation increments. The goal is to develop parameterizations of unresolved processes that will
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; experience in the use of HPC clusters) Expertise in statistical analysis, machine learning and/or data assimilation Ability to work both independently and as part of a team Very good command of written and
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. The incumbent will need to develop methods to estimate uncertainties in the outputs of the NGE OHC algorithm and run an ocean model with a data assimilation framework as well as prepare for future OSSEs using