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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 14 hours ago
Lidar and the Roscoe upper troposphere/lower stratosphere lidar). Additional projects include the development of machine learning and advanced data processing algorithms, and participation in upcoming
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 14 hours ago
to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 14 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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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 14 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 14 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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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 14 hours 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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Pest Management in The Western U.S.' (https://ai4sa.ucr.edu/ ). The overall goal of this project is to develop advanced tools for early stress (abiotic and biotic) detection and decision support for crop
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Organization U.S. Department of Energy (DOE) Reference Code DOE-Scholars-2026-ARPA-E How to Apply Click on Apply below to start your application. Application Deadline 2/9/2026 8:00:00 AM Eastern
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online databases or interactive websites. Learning Objectives: TUnder the guidance of a mentor, the participant will learn techniques in genomic epidemiology and machine learning to quantify drivers of IAV
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approaches for using machine learning to analyze X-ray data, particularly Resonant Inelastic X-ray Scattering (RIXS). The position will collaborate with experts in RIXS experiments (Mark Dean), computational