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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
between advanced sensing and analysis, enabling fast, reliable, and quantitative damage assessment of impacted composites. This project lies at the intersection of composite materials engineering, impact
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We are looking for a highly motivated candidate to pursue a PhD programme titled "CFD-informed finite element analysis for thermal control in wire-arc directed energy deposition." This research
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sources compared with gas turbines, etc. The aim of this PhD research is to develop novel performance simulation capabilities to support the analysis and optimization for sCO2 power generation systems
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, environmental science, urban sustainability, geospatial analysis, or quantitative modelling. We particularly welcome applicants who are excited about integrating ecological understanding with data-driven methods
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gain advanced training in ecological modelling, movement ecology, landscape ecology, GIS, R and Netlogo programming and population viability analysis. They will develop skills in handling large
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discipline. This project would suit someone with interests in ecology, environmental science, urban sustainability, geospatial analysis, or quantitative modelling. Experience in all areas is not required as
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university fees, and a research training grant. The doctoral researcher will gain cutting-edge skills in AI, ecology, and environmental data analysis within a collaborative, interdisciplinary environment at
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sensors to deliver resilient, high-accuracy positioning. The project sits at the intersection of navigation, AI-enhanced signal and data analysis, and wireless communication systems, with applications in
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fields, and risk damaging the part during fabrication. Finite element analysis (FEA) models, while capable of delivering detailed spatiotemporal distributions of thermal variables, suffer from limited
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year. Working at the intersection of water engineering, environmental microbiology, robotics, and lifecycle analysis, you will evaluate autonomous underwater skimming robots that minimise energy use in