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sustainability. The research will delve into power-aware computing strategies, thermal management, and the development of algorithms that balance performance with energy consumption. Students will aim to create
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The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
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of the fingers, and the positions of tactile sensors), and the control policy for that hand, when given a particular task or set of tasks. Through this, we aim to develop a framework that can automatically
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design (e.g. the number of fingers, the shapes of the fingers, and the positions of tactile sensors), and the control policy for that hand, when given a particular task or set of tasks. Through this, we
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-driven algorithms which can solve state estimation problems in fluid mechanics, such as inferring the instantaneous state of a fluid’s velocity field from sensors embedded in its boundary. The research
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transmission methods (wired or wireless) will be optimised for robust data capture in natural sleep environments. AI-Driven Analysis: Develop advanced AI algorithms to analyse the collected sensor data, aiming
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, thermal, electromagnetic or kinetic), are critical for the sustainable operation of wireless IoT devices and remote sensors. The world can reduce reliance on batteries and fossil-fuel-derived power if more
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. Key Accountabilities • Design and develop embedded AI algorithms for appliance profiling using smart meter data • Benchmark performance against state-of-the-art NILM approaches using datasets like
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-time sensing, multi-sensor fusion, and intelligent algorithms can jointly enable safer, greener, and smarter rail operations. Key research topics include eco-driving, environment cooperative perception
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and Durham University. The primary focus will be on designing and implementing deep learning and anomaly detection algorithms to analyse large-scale, real-world sensor data collected from in-service