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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
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 2 months 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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resources and warming climate. The ideal candidate will have highly developed skills in any of the following areas: hydrologic and/or water resources modeling, data science and machine learning with strong
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notebook, prepare publication-quality manuscripts (including figures and tables) and publish in peer-reviewed literature. Read literature articles, develop new ideas, and assimilate the information into his
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(data assimilation, machine learning, etc.) Writing proposals / securing external research funding Writing and submitting scientific papers Leading a research group Supervising students Participating in
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statistical analysis and modeling techniques such as Gaussian process modeling, data assimilation, and Bayesian analysis; and 4. Open-source scientific software development. Expertise in computational
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literature articles, develop new ideas, and assimilate the information into his/her project design/interpretation. Dissemination of studies: Interact on a regular basis with their PI, faculty mentor and
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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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, 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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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 8 days ago
, modeling and data assimilation, and developing digital twin technologies. Candidates can expect to work with collaborative and dynamic environment that includes fundamental and applied research performed