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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 months ago
of radiance data from new hyperspectral infrared instruments such as IASI-NG, MTG-IRS Enhancement of CrIS radiance assimilation algorithm are highly encouraged. - Use machine learning methods to cope with model
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Organization U.S. Department of Energy (DOE) Reference Code DOE-CMEI-RPP-2025-Fall-MEF-Postgrad How to Apply To apply, click Apply at the bottom of this page. Connect with ORISE on the GO! Download
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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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a U.S. veteran, service in the U.S. military, sexual orientation, gender identity, or associational preferences. Education Requirement: Education Requirements: A PhD in Educational Measurement and
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eligible for, including health insurance, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following link: https
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must have the following essential skills: Strong background in plant breeding and genetics Strong computer skills and proficiency in R or other programming languages Outstanding interpersonal and
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a unique opportunity to develop cutting-edge high-performance computing (HPC) that incorporate machine learning/artificial intelligence (ML/AI) techniques into visualizations, enhancing the efficiency
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Sciences Directorate, at Oak Ridge National Laboratory (ORNL). This position presents a unique opportunity to develop cutting-edge high-performance computing (HPC) and machine learning/artificial
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Science, Computational Biology, Bioinformatics, or a closely related field. Experience with computational modeling, statistical programming (Python, R, MATLAB), or machine learning. Experience working with large datasets
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 months ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward