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paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported Collaborative Learning (CSCL), Computer-Supported
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Brushless Doubly Fed Machines (BDFMs) are emerging as a promising technology for offshore wind turbine generators due to their fractional-sized power converters, elimination of rotor brushes and
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AI and data science, particularly in dynamic settings where observations are collected sequentially and decisions influence future outcomes. This project will develop novel machine learning and
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strong interest in machine learning/artificial intelligence. You have experience with deep learning. You have experience with reinforcement learning (preferred). You have experience with explainable and/or
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and cooperation. You have a strong interest in machine learning/artificial intelligence. You have experience with deep learning. You have experience with reinforcement learning (preferred). You have
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and cooperation. You have a strong interest in machine learning/artificial intelligence. You have a strong interest in graph-based learning (e.g., graph neural networks). You have experience with deep
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. You have a strong interest in machine learning/artificial intelligence. You have experience with deep learning. You have experience with reinforcement learning (preferred). You have experience with
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About the project: Machine learning accelerated Inverse Design of Graphene Nanoribbons for Green Energy Supervisor: Dr Sara Sangtarash, University of Warwick Thermoelectric materials convert heat
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attention to quality, integrity, creativity and cooperation. You have a strong interest in machine learning/artificial intelligence. You have a strong interest in graph-based learning (e.g., graph neural
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the field of Machine Elements. The main aim of this PhD student position is to strengthen the newly started research on Triboelectrictive nanogenerator (TENG)-based smart lubrication in Machine Elements