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/learning based techniques in the areas of robotics, or autonomous systems, • interested in autonomous systems and signal processing, • Keen to work with equipment and embedded
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signal processing methods and a modelling environment, aided by unique hardware-in-the loop, to assess the detection and estimation algorithm performance and determine optimal multistatic configurations
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capable of leveraging signals from terrestrial base stations, non-terrestrial networks such as LEO satellite, and complementary on-board sensors. Specifically, it will: To design reconfigurable airborne
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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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this research is that it should be possible to significantly improve the performance of extreme learning and assure safe and reliable maintenance operation by integrating this prior knowledge into the learning
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This PhD opportunity at Cranfield University invites candidates to explore the integration of AI into certification and lifecycle monitoring processes for safety-critical systems. The project delves
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of the key processes featuring PFAS strategy plans. It is a widely implemented process with well-known infrastructure and operation. However, while GAC regeneration frequencies for micropollutants such as
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, operation, and monitoring to reduce land requirements, optimise nutrient removal, and deliver broader ecosystem benefits. The research will combine field monitoring at a newly constructed wetland with
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industrial partners, (Boeing, BAE Systems, Rolls-Royce, Meggitt, Thales, MOD and Alstom); and from EPSRC. The investment, over the first 5 years of operation, was approaching £10M. We are now in our eighth