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Tomography (ToF-PET) offers vital functional and molecular insights for improved cancer staging, its current capabilities are often limited by the timing resolution and sensitivity of existing detector
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sources compared with gas turbines, etc. The aim of this PhD research is to develop novel performance simulation capabilities to support the analysis and optimization for sCO2 power generation systems
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maintenance. However, current technologies are relatively slow and not capable enough to provide quick performance, diagnostic and prognostic predictions for real time applications. With the rapid development
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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intelligent systems aim to optimize power usage without compromising performance, employing strategies like power-aware computing and thermal-aware optimization. These systems are crucial in extending
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FPGA/ASIC architectures that dynamically reconfigure based on AI workloads, optimizing performance, energy efficiency, and functionality. 3- Intelligent Interface Design: Create smart interfaces
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image velocimetry approaches. This enhanced understanding is crucial for optimizing performance, and educate the design of future architectures. Additionally, the research accelerates the design and
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Hydrogen has the potential to play a crucial role in decarbonising aviation in the long term, and to bring a revolution in air transport comparable to that of electric vehicles in the automotive
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trustworthy operation of navigation systems in complex, GNSS-denied scenarios. The ultimate goal is to provide the navigation research community and industry with tools and methods that ensure continuous, high
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frameworks are critical to ensuring safe, resilient, and trustworthy navigation in transport and other domains. This project will aim to enhance the performance and robustness of autonomous navigation