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. Expect close collaboration with industrial experts and the opportunity to see your algorithms influence aerospace and other high-value manufacturing sectors. Funding and eligibility 3-year, full-time PhD
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. Expect close collaboration with industrial experts and the opportunity to see your algorithms influence aerospace and other high-value manufacturing sectors. Funding and eligibility 3-year, full-time PhD
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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
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) sensor data. This will be a small system-on-chip designed to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated
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to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated into smaller, faster, more energy efficient and cost-effective hardware
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simulations are plagued by the same slow relaxational dynamics. Through collaboration across Engineering, Statistics and Chemistry, this project will develop state-of-the-art simulation algorithms to circumvent
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, memory, and energy requirements. The successful candidate will explore novel algorithms and model-design strategies that allow AI systems to operate effectively on edge devices, clinical environments
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are poised to re-define our future mobility. However, full autonomy is not possible without all-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms
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is to develop machine-learning-based algorithms for transmitter pre-distortion and receiver post-distortion architectures that enable distortion-free quantum communication systems. A key focus will be
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of speeds from walking to maximal sprinting. Derive and compare algorithms to auto-detect key frames of foot contact and toe-off, required to quantify contact, flight and swing times. Compare lower