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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 2 hours ago
and/or issues using discretion; experience with tritium transport modelling, hydrogen in materials, or fusion blanket concepts; familiarity with data assimilation, uncertainty quantification, or large
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this objective through fundamental studies on how nanoparticles can be tailored to interact with plant tissue and thereby improve the uptake and assimilation of essential mineral ions. The vision is to provide a
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CRSA - Postdoctoral position on high- resolution root zone soil moisture estimation by assimilation of microwave-derived surface soil moisture About UM6P: Located at the heart of the future Green
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the whole data assimilation and modeling process. * Other Duties as assigned. Special Notes: The Research Foundation of SUNY is a private educational corporation. Employment is subject to the Research
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uncertainties and result errors throughout the whole data assimilation and modeling process. ● Other Duties as assigned. Special Notes: The Research Foundation of SUNY is a private educational corporation
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uncertainties and result errors throughout the whole data assimilation and modeling process. ● Other Duties as assigned. Special Notes: The Research Foundation of SUNY is a private educational corporation
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-conifer-trees ), we use cutting edge technology to investigate the dynamics of freshly assimilated carbon and its transport from the assimilation sites in mesophyll of conifer needles, through the transfer
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, nutritional biofortification and functional enrichment, human nutrition, obesity or maternal-child health. Ongoing projects in plant biology include: micronutrient uptake and assimilation, secondary metabolite
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environment using models and data assimilation. We study the fundamental processes in the near-Earth environment and focus on understanding fundamental processes responsible for the evolution of space radiation
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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