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types. Your algorithms will first be evaluated through simulation using real operational datasets, and later deployed and tested at two physical facilities: a kW scale testbed at TU Delft’s Green Village
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(IQU, Spain) DC2: Design of novel distributed algorithms for CSI estimation (KUL, Belgium) DC3: Synchronization methods for CF networks (KUL, Belgium) DC4: Design of holistic neuromorphic solutions in 6G
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Job Description Are you interested in working at the intersection of optimal control, AI algorithms, and game theory, on problems that matter for real-world critical infrastructure? This PhD
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plus. You enjoy working with complex, multimodal datasets and developing robust algorithms for continuous monitoring and predictive modelling. You are comfortable combining coding, data analysis, and
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within the Norwegian Center on AI for Decision (aiD), benefiting from broad and divers expertise, and strong industrial connections. The project will have theoretical and algorithmic developments, software
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research
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. The project will have theoretical and algorithmic developments, software developments, and industrial case studies. Duties of the position Complete the doctoral education until obtaining a doctorate Carry out
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collaborators. Tasks include formulating optimisation problems, developing algorithms for optimisation with Bayesian models, and implementing solutions in relevant software. Further tasks include the formulation
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environments (preferred). You possess the ability to conduct independent research and develop novel algorithms. You have strong analytical and problem-solving skills. You have a research-oriented mindset and
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will develop and evaluate fault detection and fault location algorithms for these systems. The project is funded by GE Vernova under a wider collaboration with Imperial College London. You will be co