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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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training programme at the start of the PhD to develop skills in areas such as programming, data analysis, machine learning and signal processing. This will provide the technical foundation required to work
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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
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, machine learning, deep learning, high powered computing (requiring Python etc) or a combination of data science and qualitative methods (e.g., interviews and focus groups). Project themes include (but
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-driven AI models that capture the underlying process–structure–property relationships governing metal additive manufacturing. By combining mechanistic modelling, in-situ sensing, and machine learning
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. The project will provide the student with extensive training in large-scale data integration, machine-learning methods, field-based environmental monitoring and eDNA analysis, as well as experience working
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condition monitoring data, and help shape innovative solutions that enhance the reliability, resilience, and long term performance of next generation HVDC networks. Entry Requirements and further details
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diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems
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/structural/mechanical engineering with experience and interest in structural dynamics, vibrational analysis, train-track-bridge interaction, signal processing, data science and machine learning. The successful
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- 2025 - Business Strategy and the Environment - Wiley Online Library Additive Manufacturing: A Comprehensive Review Big data, machine learning, and digital twin assisted additive manufacturing: A review