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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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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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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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targets the development of advanced coatings to prevent cell-to-cell propagation during runaway events. It combines experimental studies, numerical modelling, and real-world burner rig testing, culminating
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, scalability, and adaptability to various applications such as autonomous systems, IoT devices, and wearable technologies. Research Focus Areas: 1- Neuromorphic and AI-Optimized Processors: Design AI-specific
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operation costs significantly. Besides, there is an opportunity to explore the commercialisation paths of the developed smart sensor prototype. You will gain from the experience in numerous ways, whether it
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deploy these technologies in the industry context without the need for big datasets. You will gain from the experience in numerous ways, whether it be transferable skills in the technical area of
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, ultimately optimising the deposition process. Additive manufacturing (AM) is a rapidly advancing technology, driving numerous innovations and finding diverse applications across industries such as aerospace
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cyclic loading, varied surface conditions, and exposure to gaseous impurities, and advanced numerical modelling (Finite Element Analysis), this project aims to significantly enhance our understanding
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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