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, ultimately optimising the deposition process. Additive manufacturing (AM) is a rapidly advancing technology, driving numerous innovations and finding diverse applications across industries such as aerospace
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and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive
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solutions for the UK agri-food sector. This interdisciplinary project sits at the intersection of bioengineering, heat and mass transfer, predictive modelling, postharvest systems engineering and applied
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the £65 million Digital Aviation Research and Technology Centre (DARTeC), leading advancements in aircraft electrification, autonomous systems, and secure intelligent hardware. Through collaborations
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Aviation Research and Technology Centre (DARTeC), leading advancements in aircraft electrification, autonomous systems, and secure intelligent hardware. Through collaborations with the Aerospace Integration
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Technology Centre (DARTeC), leading advancements in aircraft electrification, autonomous systems, and secure intelligent hardware. Through collaborations with the Aerospace Integration Research Centre (AIRC
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nutrient removal with biodiversity benefits. Optimising these systems is critical to enhance their environmental performance, support regulatory compliance, and contribute to resilient, low-carbon water
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related discipline. This project would suit a candidate with a background in mechanical, control or aerospace engineering, physics, mathematics, or other relevant engineering/science degree. The ideal
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management of auxiliary power systems. Cranfield 737-400: Aircraft Instrumentation and Environmental Control Systems (AID, ECS): A full-scale Boeing 737-400 aircraft equipped with instrumentation for studying
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
developed a dataset by conducting high-velocity impact experiments on CFRP specimens using controlled testing setups. The multimodal dataset is to be processed using X-ray CT scans, SEM imaging, and