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at leading international conferences and publish in top-tier journals. The successful candidate will gain advanced expertise in multi-sensor fusion, signal processing, machine learning, and positioning
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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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training programme in respect of industry-specific skills, and access to hotfire facilities at Westcott, Machrihanish, and elsewhere. You can learn more about the programme at r2t2.org.uk. Kick stages are a
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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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significant impacts on reducing machine downtime in many industrial applications, including steel casting, paper mill, food industry, wind energy, etc., which leads to reductions in operation and maintenance
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learning environment. Based at Cranfield University, a global leader in aerospace research, the researcher will be within the Centre for Propulsion and Thermal Power Engineering which has considerable
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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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, these systems serve as complex functional approximators trained over an input-output data set. ‘Second Wave AI’ is the term used to describe the current glut of 'machine learning' style intelligence, where
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infrastructure components, causing potential reliability, safety, and longevity issues. Addressing this critical issue represents a vital area of research in aerospace materials science and hydrogen engineering
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the impacts of intermittent discharges, such as sewage overflow (SO) spills on our natural watercourses. This cutting-edge research will look at how to engineer these green technologies to maximise