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verticals, Unmanned Aerial Vehicles, Integrated Satellite-Space-Terrestrial Networks, Quantum Communications and Key Distribution, Spectrum Management and Coexistence, Tactile Internet, Earth Observation, and
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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of public data. Key challenges include balancing privacy guarantees with utility, designing efficient algorithms for real-world applications, and assessing security and business implications in practical
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technologies used in and connected to grids (e.g., distributed generation, storage, electric vehicles, heat pumps, smart energy management systems, etc.) and how to design grid tariffs that align grid and market
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use by the group employ multiple-input multiple-output (MIMO) technology and can be connected to build a distributed and cooperative network. To develop signal processing techniques, the group
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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(PCOG) at SnT, headed by Dr. Grégoire Danoy. PCOG specializes in optimization techniques, with a focus on distributed computing and AI-driven solutions PCOG conducts research in parallel computing, search
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. This new technique, combining a novel material coating with a camera-based tracking system, aims to provide a unique spatially-distributed bridge monitoring, leading to new understandings of structural
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) to assess the temporal and spatial distribution and identify hotspots of O3/CH4 pollution in forested areas across Luxembourg, (c) to create a geospatial database of O3/CH4, crops, and (dead) forest trees by