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Instrument for Magnetic Sounding (PIMS) on the Europa Clipper Mission. Space Sci Rev 219, 62 (2023). https://doi.org/10.1007/s11214-023-01002-9 3. Kataoka, R., Nakano, S. & Fujita, S. Machine learning emulator
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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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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
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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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burn chamber. https://research.fs.usda.gov/firelab At the heart of the USDA Forest Service's mission is their purpose. Everything they do is intended to help sustain forests and grasslands for present
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Laboratory in Quantico, Virginia. The program provides a hands-on STEM learning experience, utilizing state-of-the-art equipment and mentorship from esteemed experts across diverse STEM fields that support
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, austere conditions. Learning about military deployment health and gain experience in environmental data collection. Contributing to solutions for difficult environmental health problems in complex