23 embedded-systems-"https:"-"https:"-"https:"-"https:" scholarships at Cranfield University
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way offshore assets are monitored and maintained, enabling safer, smarter and more sustainable renewable energy systems. This project sits at the intersection of offshore engineering, digital
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technologies for sustainable offshore wind systems. The research will combine digital twins, artificial intelligence, predictive maintenance, life cycle assessment and techno-economic analysis to improve the
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tuition fees. This PhD project in the area of autonomy, navigation and artificial intelligence, aims to advance the development of intelligent and resilient navigation systems for autonomous transport
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Are you interested in shaping the future of zero-emission transport systems? This PhD study, fully funded by the Faraday Institution, facilitates the use of next-generation Lithium-Sulfur (Li-S
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intersection of aircraft systems design, thermal management, and artificial intelligence/machine learning. It focuses on electrothermal ice protection systems (ETIPS), which prevent hazardous ice
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community at WAMC, fostering collaboration and innovation. Additionally, there will be opportunities to work with WAMC’s industrial partners, such as WAAM3D ( https://waam3d.com/ ) and members of WAAMMat
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is a crucial aspect of powerplant integration, operability and performance. This applies to podded as well as embedded propulsion systems. The standard approach to measure non-uniformities of the
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. Smart grid. Whole systems. AI for energy systems. Carbon capture, utilisation and storage (CCUS). Low-carbon fuels and chemicals. Renewable Energy. Marine Engineering. Energy storage. Waste-to-X and
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An exciting opportunity to work on an interdisciplinary 4-year PhD studentship, fully funded by EPSRC Industrial Doctorate Link Award (IDLA) in collaboration with BAE Systems, open to Home (UK
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postharvest drying energy demand. Combining applied mycology, food safety modelling, precision agriculture and Net Zero energy systems, the research will deliver energy-efficient, data-driven grain