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Computer Engineering or a closely related field, completed by the start date of the appointment. U.S. citizenship is required. Research experience in at least one of the following areas: ● Chip design
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to develop AI-enabled, low-latency signal-processing algorithms for next-generation pixel detectors used in high-energy physics experiments. This position offers the opportunity to engage in cutting-edge
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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 months ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development. Join the National Laboratory of the Rockies (NLR), where world-class scientists
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routine background checks. Essential Duties and Responsibilities Neuroimaging data collection and management Data analysis and model building Develop advanced deep learning and machine learning algorithms
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unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In
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, Environment, and Community Health (CTECH)—a U.S. Department of Transportation Tier 1 University Transportation Research Center dedicated to advancing sustainable technology-enabled mobility for public and
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and to develop novel and improved platforms for quantum computation and communication and thus strengthen U.S. leadership in QIST. This calls for expertise across disciplinary sciences – encompassing
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responsibly. What You’ll Work On You will contribute to the development and evaluation of ethics-centered adaptive brain/muscle–machine interface technologies using physiological signals (primarily sEMG