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Field
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addresses the design of wholesale, balancing, reserve, and capacity markets under high shares of variable renewable generation; price formation, scarcity pricing, and locational signals; network charging and
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leverages AI and cutting-edge infrastructure to optimize EV charging and energy systems. By integrating distributed energy resources, demand response, and storage, it aims to enhance grid flexibility and
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unique opportunity to work on innovative solutions to address global challenges; freedom and focus to conduct creative research while making an impact in relation to ESA’s strategy; a wide network of
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 2 months ago
observations into accessible products for research, management and policy. Your Tasks Compile, curate and analyse existing Central Arctic Ocean and Southern Ocean datasets on fish distribution and ecosystem
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communications (ISAC) in distributed multiple-input multiple output (D-MIMO) systems for resilient and efficient intelligent transport systems (ITS) and 6G-integrated non-terrestrial networks (6G-NTN)! Chalmers
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
. Leglaive, L. Girin, X. Alameda-Pineda, and R. Séguier, "A multimodal dynamical variational autoencoder for audiovisual speech representation learning," Neural Networks, 2024. 10. A. Ballou, X. Alameda-Pineda
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single node between multiple secure workloads. Investigate and evaluate mechanisms for secure encrypted communication across RDMA based networks. Design and evaluate key distribution and management
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. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust
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will focus specifically on investigating hillslope-riparian zone-stream connectivity at the reach-to-catchment scale for a better understanding and spatially distributed prediction of subsurface
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such as transformers, self-supervised learning, multimodal learning, generative models, graph neural networks, or foundation models. Experience with structural and/or functional brain modeling. Familiarity