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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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. when do we stop modelling? How do we track / score the quality of the model? What is the required level of quality over time? How can quality be brought to the required level? Can Machine Learning, Large
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health monitoring, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical skills and technical expertise, enhancing their research capabilities
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. •Specialist training in AI, machine learning, and digital engineering. •Collaboration with academic and industry experts for technical insight and mentoring. •A supportive research environment focused on both
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learning models, especially when new training experiences are corrupted. The framework will be validated in robotic control scenarios during EV battery assembly. As a PhD student, you will work with both
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science, along with proven skills in prototyping software using real-time 3D engines and implementing machine learning models. With 50+ researchers and PhD students, the Centre for Sustainable Cyber Security (CS2
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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to date focus on just one layer, understanding what keeps AF going is challenging. This PhD project aims to bridge that gap by combining advanced machine learning tools with a new experimental protocol
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integrating Machine Learning (ML) with physics-based degradation modelling will enhance early fault detection, reducing unplanned downtime. This PhD is hosted at Cranfield University, a global leader in
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configurations. Machine learning techniques will be incorporated to dynamically adjust PST settings in response to evolving grid conditions. This multi-layered approach aims to bridge the gap between static