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Field
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Packages/Procedures: Update, review, manage, and run daily, monthly, and quarterly reporting packages, with a keen eye on data quality and robustness, and distribute them to relevant parties within
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 5 hours 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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Research Infrastructure? No Offer Description Mission: Develop perception algorithms that allow estimating the state of a mobile robot; monitoring and presentation of results. Functions to be developed
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Location ESRIN, Frascati, Italy Description Data Quality and Cal/Val Manager for Atmospheric Sensor Missions in the Sensor Performance, Products and Algorithms Section, Earth Observation Mission
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; Develop models and algorithms for energy-aware scheduling, workload prediction, and performance–energy trade-off optimization; Investigate network system energy efficiency, including traffic scheduling
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Responsibilities: Conduct individual research within the designed project: process data, develop research methods, build and evaluate computer vision and machine learning algorithms empirically. Author research
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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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networks, online analysis with delay, and theory of distributed algorithms. Job description In our group, we try to apply and unite the approaches and techniques of theory and practice. Some members of our
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that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance and edge computing; The design of architectures
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 23 days ago
follows a phased algorithm: 1) generate an initial training set by uniformly sampling input points 2) (re)train the model on the trainng set 3) use feedback from the model’s performance to generate/augment