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Field
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shift in the world of hardware design. On the one hand, the increasing complexity of deep-learning models demands computers faster and more powerful than ever before. On the other hand, the numerical
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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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an increasingly complex development environment. Areas to consider that impact the modelling are: Framework Language Process How wide / how deep i.e. what do we model and why? How much provides a good answer i.e
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solutions need to be safe and accurate. Aim This project will focus on investigating and developing new ways in which deep learning-based solutions can continuously learn and deal with unseen situations, with
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deep learning methods to enhance the predictions beyond existing data. By incorporating microstructural features into predictive models, the aim is to create a reliable data-driven modelling framework
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simulation regimes by harnessing and advancing the latest developments in AI Machine Learning. This studentship is a continuation of prior work that is looking at using new cutting-edge deep learning models
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understood how such automation solutions can be safely and robustly supported with state-of-the-art deep learning. There is a need for new AI that can incrementally learn and adapt without losing accuracy
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, analytical and computer programming skills. Advantage will be given to applicants with experience in one or more of the following: signal processing, deep learning, acoustics, psychoacoustics, acoustic
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
relationships, together with deep domain expertise. These methods open new possibilities for extracting and connecting knowledge at scale. The goal is to enhance digital twins with the capability to interpret
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turbulence, and use this knowledge to identify control strategies through deep reinforcement learning. The methods developed in this project will directly contribute to designing novel porous media