894 machine-learning "https:" "https:" "https:" "https:" research jobs in United States
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programming, algorithm development and deep learning model implementation, and practical experience in drone and boat-based surveys are preferred. Background Investigation Statement: Prior to hiring, the final
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, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race
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. Familiarity with machine learning (ML) techniques for optimizing models or analyzing complex datasets. Experience with space-based instrumentation or similar high-precision systems. Demonstrated expertise in
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 5 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 5 hours 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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at https://puwebp.princeton.edu/AcadHire/position/40281 and submit a current curriculum vitae, research statement, and a cover letter. Contact information for three references is required. To learn more
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, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control the dynamics of microbial communities in time and space. Ongoing projects
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to quickly learn and master computer programs. Ability to work under deadlines with general guidance and manage multiple research tasks simultaneously. Excellent organizational skills and demonstrated ability
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, the participant will learn HPC computing technologies and techniques in genomic epidemiology and machine learning to quantify drivers of IAV evolution in swine using data generated from IAV surveillance in human
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machine learning. The specific goal is to extend new and existing visualization environments to support efficient and precise annotation of histopathology images using a combination of expert human review