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, complexity, and harsh operating conditions. This PhD research addresses two critical challenges in this domain: (1) optimizing sensor movement for inspecting large and complex equipment using robots and
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drive the gradual development of these technologies toward real-world applications. This involves engineering experimental hardware for cell culturing workflows, optimizing experimental processes, and
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process. Address blind inverse problems by defining a network to learn distortion functions from data, informing the optimization in the learning process. Refine optimization and learning strategies
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and brain tissue mechanics to improve stroke treatment. Stroke is a leading cause of death and disability worldwide, making advancements in its diagnosis and treatment highly relevant. Computational
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This is a self-funded opportunity relying on Computational Fluid Dynamics (CFD) and wind tunnel testing to further the design of porous airfoils with superior aerodynamic efficiency. Building
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research which combined efficient optimization and sequential reliability assessment. The project is funded through an EPSRC call to accelerate research outcomes to achieve a prosperous net-zero and is
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in a more accurate analysis of optimizing the service performance. Computer vision approaches such as ones for object identification and action recognition can help to automatically identify deviations
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that values equity, diversity, and inclusion, gaining unique expertise in aerospace systems design and integration (airframe, engine, subsystems), system of systems optimization, multi-fidelity models
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changes (so called swelling). Swollen batteries are at risk of rupturing which may significantly shorten their lifetime. Development of advanced computer models is critical for understanding and
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Aviation by 2050. This exciting doctoral project, in collaboration with Rolls-Royce, will develop innovative computer vision methods which when combined with optical flow velocimetry will enable imaging