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electric mobility and smart energy management. We design and implement charging infrastructures and advanced energy optimization systems, combining engineering, proprietary software, and strategic vision
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Intelligence Unit looking for talented and motivated candidates to work on applying AI techniques to model, predict and optimize energy systems. Join us at the heart of a mission that truly matters: transforming
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optimization of energy performance for facilities and cooling systems that support NERSC HPC operations. The position will collaborate with the LBNL Energy Manager (Sustainable Berkeley Lab Office) to help meet
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. The thematic orientation of its research concerns aerodynamics, solid mechanics and energy conversion in flows including plasma and lasers. Participation in the design and optimization of integrated energy
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control logic. - Optimization of resource usage, energy consumption, and timing performance (timing closure). Where to apply E-mail a.gutierrez@upm.es Requirements Research FieldEngineering » OtherEducation
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.) must contribute to the effort to reduce GHG emissions. Clouds, despite their ability to optimize processes in other sectors (transport, energy, agriculture), are no exception to this observation
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Laboratoire Interdisciplinaire des Energies de Demain - Université Paris Cité | Paris 15, le de France | France | 14 days ago
3 Apr 2026 Job Information Organisation/Company Laboratoire Interdisciplinaire des Energies de Demain - Université Paris Cité Research Field Chemistry Physics Researcher Profile Recognised
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technology and energy to transform society and industry through training, research and innovation. It aims to be the leading French higher education and research institution in this field on an international
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science, industrial engineering, or a comparable field (Master’s degree) Knowledge of modeling, optimization, and data analysis of energy systems Interest in electricity markets, energy system analysis, and model-based
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optimization-based network partitioning point to scalable, communication-aware control designs; stochastic MPC and co-design studies demonstrate methods for handling uncertainty and jointly optimizing assets and