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Field
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of Programmable and Intelligent Networks Position You will work actively on the preparation and defence of a PhD thesis Edge Intelligence for 6G Networks. The PhD project will deep dive into Edge Intelligence
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institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates training, exchanges, and career development for PhD students and postdocs. Learn more
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%) doctoral scholarship holder in the field of Programmable and Intelligent Networks Position You will work actively on the preparation and defence of a PhD thesis Resource-Aware AI for Next-Generation Networks
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of systems for transportation utilising electric drive (cars, trains, ships, aircrafts) or transformation for high-voltage DC power distribution networks. Currently, great research effort is focused on growth
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to develop scalable and energy-efficient AI accelerators for resource-constrained IoT and networked systems through joint optimization of AI models and emerging hardware architectures. Topics will include: AI
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Identify suitable application task in the field of geolocation and optimize network and learning rules accordingly Design, set up, and operate experimental systems for circuit-level measurements and data
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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of Materials, analytical and numerical Data-Driven Engineering Design and Optimization Algorithms Surrogate Modeling (e.g., Kriging, Gaussian Processes, Neural Networks, etc.) Scientific Programming (e.g
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CHP units, the wider energy system risks losing a crucial flexibility resource. Through strategic integration hybrid energy storage systems with greenhouse operations via optimization methods, control
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applications. Grid-interactive efficient buildings rely on emerging digital and cyber-physical systems to optimize HVAC operations for both energy efficiency and demand response. While Model Predictive Control