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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
will train machine learning models to identify and assess internal defects with greater accuracy and speed than traditional methods. The results will support predictive maintenance, reduce inspection
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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validation with end-users. The student will have access to specialised training in quantum security and advanced machine learning. The self-funded nature of the project affords the unique flexibility to pursue
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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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failures before they occur, enabling proactive maintenance strategies. Anomaly Detection Mechanisms: Implement machine learning techniques to identify and classify anomalies in electronic systems, enhancing
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, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical skills and technical expertise, enhancing their research capabilities and employability in
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aircraft, utilized for research into thermal management and system health monitoring, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical
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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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should have a strong foundation in artificial intelligence, machine learning, and multi-agent systems, along with experience in programming, data analysis, and model development. Knowledge
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for research into thermal management and system health monitoring, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical skills and technical