156 machine-learning-"https:" "https:" "https:" "https:" "https:" "The University of Edinburgh" uni jobs at Zintellect in United States
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. Description: Theoretical research and computer simulation are carried out with emphasis on observations of space plasmas. Specific interest areas include (1) nonlinear phenomena in unstable collisionless
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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
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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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. Description: Research opportunities exist within the applied Earth sciences and applications of Earth remote sensing to employ data from the upcoming NISAR mission (https://nisar.jpl.nasa.gov/) to develop and
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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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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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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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, active geologic processes, vegetation traits, and algal biomass using hyperspectral imagery in the visible and shortwave infrared and multi- or hyperspectral imagery in the thermal IR (https
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capbilities include testing on high altitude balloons. For the past year we have hosted the award-winning Stanford-Brown iGEM team, whose wikis will give additional background in lab activities. http://2011