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forward. They all require enhanced processing power and efficiency. Therefore, many of the sub-domains within Electrical Engineering, like advanced computing benefitting from novel chip designs, sensors and
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the loop and using active learning to determine which demonstrations to collect. The candidate would work on both projects and be responsible for: Implementing AI and probabilistic ML algorithms Development
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(entities) given the rules and the rules given the molecules. The aim of this project is to develop a theory and accompanying algorithms to decide if an abstract system can be instantiated by a concrete
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electricity price signals, demand-response mechanisms, and time-of-use optimization. AI-Driven Optimization using Reinforcement Learning: Apply RL algorithms to develop and train agents that optimize power
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and efficiency. Therefore, many of the sub-domains within Electrical Engineering, like advanced computing benefitting from novel chip designs, sensors and embedded intelligent systems as well as next
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background within (at least one, preferably several of) the following fields: Biomedical circuits and systems Physiological Modelling Medical sensor and instrumentation technology Health Informatics Digital
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algorithm. Design methods: Develop novel control methods for power electronic converters feeding electric machine Simulation: Learn advanced simulation tools such as Ansys to simulate and analyze the effect
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-based topology optimisation and de-homogenisation Adaptive meshing algorithms for topology optimization PDE-driven topology optimisation methods Research fund application Collaboration with industrial
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, utilizing cutting-edge reconfigurable computing and sensors. The aim is to develop next-generation electronics and computing solutions that solve technology-related challenges in fields such as autonomous
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from our industrial partners, the candidate will grow a hands-on expertise in robotic production operations, including robots, tools, and sensors. Contact information For further information, please