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. This project will build upon our research and industrial successes, focusing on developing control solutions for automated robotic systems that can be teleoperated using intuitive human-machine interfaces
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and signal processing methods using machine learning techniques to enhance the resilience, efficiency, and security of cell-free massive MIMO systems, which are expected to play a key role in next
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modes (e.g., HCCI) for net-zero fuels like hydrogen and ammonia. A key innovative pillar is the development of an AI-driven control strategy. Machine learning algorithms, including reinforcement learning
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Supervisors Primary Supervisor - Dr Calum Williams Secondary Supervisors - Dr. Maciej Dabrowski , Prof Simon Horsley This PhD studentship will develop 3D-printed optical metamaterials to control
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and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive
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in physics is not possible without acknowledging the crucial role of observations and context in the foundations of quantum theory. If you are interested in deepening our conceptual understanding
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not possible without acknowledging the crucial role of observations and context in the foundations of quantum theory. If you are interested in deepening our conceptual understanding of the intersection
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excellent understanding in power system dynamics, power electronics and control. Experience in working with power systems dynamic simulation will be helpful, but not necessary to apply for the position. How
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Funding amount: Funding will cover tuition fees for UK students (at the Home rate), a stipend at the UKRI rate , and a consumables budget. This is a 3.5-year PhD studentship funded by the School
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that experiments have historically been conducted without precise control over the environmental conditions, allowing too many variables to influence tree growth simultaneously. For decades, the field