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including number theory, dynamical systems, probability theory, equidistribution theory and optimal transport. The aim of the PhD project is to develop a flexible framework based on harmonic analysis to study
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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; implementation of digital twins that enable real-time decision optimization; and establishment of cross-industry frameworks that allow technology transfer between sectors. Your findings will be published in high
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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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reach the limit of the electrical grid connections to their sites if the transition is not done in an optimal way, an issue that will be prominent in industrial, commercial and residential areas across
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Engineering, and Engineering Management. Students with interests in computational mechanics, optimization design, bioinspired design, sustainability management, machine learning, AI, uncertainty quantification
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safety questions: Determining optimal stored energy requirements for grid support, considering various timescales and power ratings. Reviewing and benchmarking storage technologies (lithium-ion batteries
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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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process. Address blind inverse problems by defining a network to learn distortion functions from data, informing the optimization in the learning process. Refine optimization and learning strategies