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Field
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control strategies. Develop and implement advanced control algorithms for real-time operation and performance enhancement of power electronic converters and transformer-based solutions. Perform hardware-in
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develop algorithms to identify and predict SRL subprocesses from multimodal learning data (e.g., EEG/fNIRS, eye-tracking, and think-aloud protocols); • Analyze large-scale learning analytics data
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development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning algorithms might support the wider integration of, and uptake
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models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in machine learning, computer vision, and medical
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Fellow in Autonomous Systems and Control to design and implement efficient, performance‑guaranteed distributed control approaches, leveraging cutting‑edge learning algorithms and AI strategies
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Experience with machine learning algorithms and ideally experience developing novel methods Understanding of basic biological principles and experience interpreting ‘omics data Ability to analyse information
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research team. Key research areas include: Development of low-carbon materials and tunable thermal energy storage materials integrated with smart sensors and advanced algorithms Creation of Digital Twins
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to research-based activities, including the development of new data analysis algorithms, processing and analysis of field data, and participation in the fieldwork. Your responsibilities will include: Conduct
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model checkers; proofs of safety and/or security properties; programming languages and/or type systems; concurrent and/or distributed algorithms; and related topics. The successful applicant will work in
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technology management, or smart grids. Experience in development of mathematical meta-models, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno