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will be validated through scaled hardware‑in‑the‑loop (HIL) laboratory experiments. Collaboration within the FME SOLAR consortium is an integral part of the position. Required qualifications Master’s
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then be evaluated using real-time digital simulators for hardware-in-the-loop experiments. Additionally, a Cyberphysical co-simulation environment will be used to assess controller performance under
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on a subset of the participating devices. Aggregate computing provides a unifying foundation for developing these systems, avoiding a centralised server and obtaining deployment on multiple hardware
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environment in Norway, and offer a wide range of theoretical and applied IT programmes of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software
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National Lab, University of Tokyo etc.), the PhD candidate is expected to research on some of the following themes: New algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient
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to research on some of the following themes: New algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process
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optimization strategies, including reinforcement learning (RL) and optimal control approaches for state preparation and measurement protocol design under hardware constraints. Develop open-source software and
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control approaches for state preparation and measurement protocol design under hardware constraints. Develop open-source software and documented benchmarks. Co-author high-quality publications and
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to the theoretical development, numerical simulation, and potential hardware-mapping of these oscillator-based systems, exploring concepts like quantum annealing, symmetry breaking, and reservoir computing
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to the theoretical development, numerical simulation, and potential hardware-mapping of these oscillator-based systems, exploring concepts like quantum annealing, symmetry breaking, and reservoir computing