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drone detection and localisation performance using radar systems. This can be achieved by improving the detection performance of individual sensors and by employing a cooperative network of sensors which
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to state-of-the-art laboratories, hardware/software resources, and design facilities, supporting AI-powered electronics research. This project will be conducted within Cranfield’s Integrated Vehicle Health
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from over 100 countries and support our staff and students to realise their full potential, from academic achievement to mental and physical wellbeing. Cranfield Doctoral Network Research students
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blend of academic and industrial training, preparing you for diverse career pathways in research or industry · Cohort Experience: Build your research network through inclusion in a vibrant cohort of PhD
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part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a
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to industrial clients such as Boeing, BAE Systems, Rolls-Royce, Meggitt, Thales, MOD, Bombardier, QinetiQ, Thales, Network Rail, Schlumberger and Alstom. Entry requirements Applicants should have a first or
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of representative failure models for gear failures causes difficulties in their useful lifetime prediction. Critical operational parameters such as loading, speed and lubrication affect the physics of gear meshing
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into a dynamic, confident and highly competent researcher with wider transferable skills (communication, project management and leadership) and an international network of colleagues. At a glance
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, project management and leadership) with an international network of colleagues. At a glance Application deadline25 Mar 2026 Award type(s)PhD Start date28 Sep 2026 Duration of award4 years (full-time) and 6
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of the art academic and research services to industrial clients such as Boeing, BAE Systems, Rolls-Royce, Meggitt, Thales, MOD, Bombardier, QinetiQ, Thales, Network Rail, Schlumberger and Alstom.