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learning, to accelerate CFD optimization and enable adaptive control strategies for complex urban wind conditions. From an industrial standpoint, the objective is to deliver a cost-effective and efficient
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conditions. To achieve this, the project explores advanced machine learning approaches, including surrogate modeling and reinforcement learning, to accelerate CFD optimization and enable adaptive control
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applying quantum computing to addressing real-world challenges? Join us to develop quantum optimization methods that support the transition to sustainable and resilient electrical power systems! Information
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further on recent achievements in our group, including single-shot nuclear readout and electron-nuclear double resonance (ENDOR). Specifically, the aim of the research is to explore means to control and
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motivation to independently formulate research projects and carry them through to completion; Mathematical skills and command of standard software packages for implementing of optimization algorithms, and a
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nuclear readout and electron-nuclear double resonance (ENDOR). Specifically, the aim of the research is to explore means to control and readout spin systems that are more resilient against decoherence from
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) and Materials Intelligence (MI) groups in the MSE department. The CTE team focuses on understanding and controlling local corrosion processes to enhance the durability and sustainability of materials
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programme will proceed in three main phases. In the initial phase, you will develop and optimize physical and numerical models describing the electron optics of the complete probe-forming column, including
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performance on an innovative VTOL platform (https://aerogriduav.com/ ). AI models to predict ship motion to optimize landing timing. You will work at the MAVLab, which is part of the Control & Simulation
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on wind turbines and ships. This involves advanced computer vision, dedicated lighting systems, optical communication, and robust control and guidance algorithms. You will work on: Design and implement