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management, cache optimization, and vectorization techniques. Strong understanding of algorithms and data structures, especially those suitable for parallel processing and distributed computing. Understanding
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topics as well as related topics (e.g., privacy, trustworthiness, mobile/wireless security, distributed learning and cloud security, software and system security). Areas of theoretical computer science
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 1 month ago
The Manager of Core Network Infrastructure is responsible for the strategic and operational oversight of core networking equipment—including routers, firewalls, and distribution switches—across
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://www.nrel.gov/grid/distributed-energy-resource-management-systems.html . Basic Qualifications Minimum of a 3.0 cumulative grade point average. Undergraduate: Must be enrolled as a full-time student in a
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multi-sensor atmospheric remote sensing from ground, airborne, and satellite platforms. Our group develops advanced algorithms and data analysis methods to address fundamental scientific challenges
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stiffness composites Implement bespoke optimization algorithms combining shell geometry and fibre distribution accounting for nonlinearity and uncertainty Conduct optimization studies and oversee
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for in-space manufacturing and testing. Develop AI algorithms for navigation, guidance, and control. Lead collaborative research proposals for NSF, DoD, DOE, NASA. Mentor students and build
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&D Signal and Information Processing Software Developer will support software development and integration of real-time mid-frequency active sonar processing capabilities, including algorithm design
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 3 months ago
to compare base approaches (no active learning) with different state-of-the-art active learning algorithms including our solutions; Participate to the redaction of a publication exposing the methodologies and
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short- and long-term demand prediction, renewable generation forecasting (solar, wind, hydro) under uncertainty, spatiotemporal modeling for distributed energy systems, energy markets, transfer learning