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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 3 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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processes. Furthermore, combining radar-derived surface soil moisture maps with a land surface model using a data assimilation approach has the potential to produce daily root zone soil moisture estimates
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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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Organization (RTO) active in the fields of materials, environment and IT. By generating relevant scientific knowledge and converting knowledge into technologies, smart data and tools, LIST empowers citizens in
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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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, 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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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