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. The system will leverage cutting-edge techniques in Natural Language Processing (NLP), Machine Learning (ML), and Multimodal Analysis to conduct adaptive interviews, assess candidate responses, and generate
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for doctoral students. Overview This PhD project focuses on developing real-world deployable Machine Learning (ML) solutions integrated into Industrial Internet of Things (IoT) edge devices for condition
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behaviours? The proposed approach will focus on developing a multi-agent AI framework that integrates traditional penetration testing methodologies with machine learning techniques and advanced generative AI
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resource-constrained environments, and it is important to investigate whether features derived from different network layers can be effectively combined. Machine Learning Model Development & Optimization
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Failure Analysis of Composite Sleeves for Surface Permanent Magnet Electrical Machines This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites
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This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites Research Groups at the Faculty of Engineering, which conduct cutting-edge research
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datasets, therefore, there will be a focus in the implementation of models for large volumes of data. The project will work in an exciting interface of statistics and machine learning and has the potential
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related discipline. Strong background/skills on machine learning, mathematics, probabilistic modelling and optimisation are preferred. To apply please contact the supervisor, Dr Mu - Tingting.Mu
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structural alloys. The project will combine advanced phase-field fracture mechanics, continuum-scale chemo-thermo-mechanical modeling, and advanced machine learning techniques for enhanced prediction accuracy
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. A non-deterministic AI machine learning model for the identical task would not offer this demonstrability or, critically, the repeatability of classical algorithm-based systems. Furthermore, there is