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the intersection of ecology, machine learning, and sustainable land management, the research will combine field data collection, deep learning model development, and stakeholder co-design to support biodiversity
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profoundly affect their mechanical properties and overall performance. Therefore, understanding the temperature field and developing effective thermal control techniques are vital to ensuring a high-quality WA
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, operation, and monitoring to reduce land requirements, optimise nutrient removal, and deliver broader ecosystem benefits. The research will combine field monitoring at a newly constructed wetland with
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paramount. Secure and trustworthy AI-electronics focus on embedding security features directly into hardware, such as hardware security primitives and tamper detection mechanisms. This field addresses
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This PhD project will focus on developing, evaluating, and demonstrating an intelligent solution of diagnosis and prognosis for rotating machinery to enhance safety, reliability, maintainability and
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for Rotating Machinery Faults: A versatile platform that replicates common faults in rotating machinery, such as imbalance and misalignment, facilitating the development and validation of diagnostic and
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, isolation, and prognostics. Machine Fault Simulator for Rotating Machinery Faults: A versatile platform that replicates common faults in rotating machinery, such as imbalance and misalignment, facilitating
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microstructure, residual stress, and distortion of the deposited parts, all of which significantly impact their mechanical properties and overall performance. Consequently, accurately determining and effectively
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focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based
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-ion batteries, rotating machinery, aircraft fuel system, APU, and electrical power generation system). A comprehensive test-bed for in-depth studies will be used for experiments for demonstration and