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settings. Develop and evaluate AI and machine learning models for automated detection of contamination and intrusion events. Interpret and validate imaging and sensor data to support evidence-based decision
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candidates who would like to learn and gain experience in how to plan and perform large animal research and/or burn research in a military medical environment. What will I be doing? As an ORISE participant
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measurements, airborne measurements from field campaigns (e.g., NASA CAMP2EX, ARCSIX) or surface observations. Research involving machine learning techniques will be strongly encouraged. Field of Science: Earth
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• Unmanned Aerial System development, testing, and research • Biomechanics research in musculoskeletal and gastrointestinal systems • Machine learning • Mechatronics and robotic systems • Wind Tunnel
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project), create a unique opportunity to apply machine learning and neural network methodologies, in conjunction with simplified ice sheet models, to advance understanding of ice sheet basal processes and
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of interest include: • Machine learning for classification of astrophysical signals • Artificial intelligence augmentation of spaceborne observatories to reduce data transmission rates • Migration of science
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at https://ntl.bts.gov/ntl. Are you interested in learning how to develop a new archival repository into a more discoverable, rich resource for the Department of Transportation? Here is an opportunity
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improve estimation of rates of snow accumulation, snowmelt, ice melt, and sublimation from snow and ice worldwide at scales driven by topographic variability. We seek projects focusing on the use of machine
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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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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