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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 4 hours 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
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minutes from the U.S. Capitol and U.S. Supreme Court, Georgetown University offers rigorous academic programs, a global perspective, and unparalleled opportunities to engage with Washington, D.C. Our
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 4 hours ago
include (but are not limited to): Develop algorithms to characterize aerosol speciation from LIDAR fluorescence signals Develop machine learning emulators to represent forward operators for polarimeter-only
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 4 hours ago
Countries can be found at: https://www.nasa.gov/oiir/export-control . Eligibility is currently open to: U.S. Citizens; U.S. Lawful Permanent Residents (LPR); Foreign Nationals eligible for an Exchange Visitor
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Prof Education Department Homepage https://rede.ecu.edu/cpe/ Advertising Department RESEARCH, ECONOMIC DEVELOPMENT AND ENGAGEMENT Division Academic Affairs Classification Title Continuing and
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discount programs Posting Summary We are seeking part time/adjunct faculty to teach in our School of Nursing-Educational Opportunity Fund (EOF) for the Fall 2025 and Spring 2026 term. We are seeking faculty
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proposals. Responsibilities Develop, implement, and evaluate new statistical and machine learning methods aligned with the two themes above. Lead and co-author manuscripts in statistical, machine learning
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: https://ansci.wsu.edu THE COLLEGE OF AGRICULTURAL, HUMAN, AND NATURAL RESOURCE SCIENCES: The College of Agricultural, Human and Natural Resource Sciences (CAHNRS) at Washington State University is an
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innovation (eg, multi-omics, machine learning, remote assessment), and enthusiasm for team science and collaboration. Faculty will work collaboratively with CHA, IFH and Rutgers leadership to develop signature
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, the participant will learn HPC computing technologies and techniques in genomic epidemiology and machine learning to quantify drivers of IAV evolution in swine using data generated from IAV surveillance in human