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of Stony Brook University. BSA salutes our veterans and active military members with careers that leverage the skills and unique experience they gained while serving our country, learn more at BNL
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tools in research. Excellent written and oral communication skills, with a proven track record of publishing scientific papers and delivering presentations. Experience with machine learning techniques
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the supervision of the PI, including proposal development and preparation of high-quality publications in top computer security, privacy, embedded systems, sensing, and networking venues. Pursue research topics
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competitive ERC. The project focuses on the development of a first-principles, machine-learning-accelerated computational framework for modelling polymorphism, anharmonicity, and electron–phonon interactions in
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 months 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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the beginning and there is still much to be learned! You will lead a project that centers on how tactile end organs assemble, function, and recover after injury. You will be using non-standard animal models
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reports, training or educational workshops, and/or mass media publications. Fellows will also be expected to teach at least one course at the undergraduate and/or master's level in support of I-GMAP's
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practical experience in machine learning, especially deep learning and its practical application in the domain of language processing and sensor analysis Solid practical experience in the field of natural
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 2 months ago
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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data using multivariate statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft