117 machine-learning "https:" "https:" "https:" "https:" "U.S" "U.S" Postdoctoral positions
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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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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 10 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 10 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) | Pasadena, California | United States | about 10 hours ago
that are facile with computationally efficient, rigorous machine learning for image region identification, demonstrate an understanding of both planetary and scalable computer science, and have publication
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 10 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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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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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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large sample libraries; performing in-depth analysis of proteins, peptides, and small molecules in beef and pork products that vary in consumer quality; and applying machine learning techniques to predict
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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