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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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, 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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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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, and more efficient operations. After all, the greenest energy is the one that’s not spent – and this project aims to unlock just that by refining the way we design and optimize airfoils. The focus
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that values equity, diversity, and inclusion, gaining unique expertise in aerospace systems design and integration (airframe, engine, subsystems), system of systems optimization, multi-fidelity models
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through advanced modelling and simulation. A key objective is to validate and optimize poroelastic finite element models of brain tissue, making them more accurate and clinically relevant. Additionally
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scalable surface engineering methods and state-of-the-art permeation analysis techniques, the project will optimize coatings for alloys such as steel, aluminium, titanium, and nickel. The project will use a
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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