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Field
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-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
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the foundation of computer vision, monitoring, and control solutions. However, real applications of AI have typically been demonstrated under highly controlled conditions. Battery assembly processes can be
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research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle Analysis
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models and physics-based models. More recently, hybrid prognostics approaches have been presented, attempting to leverage the advantages of combining the prognostics models in the aforementioned different
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with engineering, physics, mathematics, acoustics, fluids, electronics or instrumentation background. Prior experience in computational modelling is beneficial, but not mandatory. Similarly, experience
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to be considered. How to apply Apply online by clicking the 'Apply' button, above. Select programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences, next page select “PhD
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Are We Looking For? We seek a proactive and enthusiastic individual with a first-class or upper second-class honours degree (or equivalent) in: Engineering (Mechanical, Materials, or related disciplines
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apply early for the best opportunity to be considered. How To Apply Apply online by clicking the 'Apply' button, above. Select programme type (Research), 2025/26, Faculty of Engineering and Physical
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catalytic process. In addition, it is important to improve the student the experimental skill, materials characterization skill and data analysis skill. Student who joins our group will learn the fundamentals
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Programme: Hybrid CFD and process simulation for process intensification of post-combustion CO2 capture School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof