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of IAI mechanisms and develop innovative antibacterial biomaterials to improve patient outcomes. Structured around three core scientific pillars—regenerative medicine, biomaterial science, and
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emerging photonic microdevices promising to revolutionise computer, communication, and sensing technologies must be performed with unprecedented picometre (one-hundredth of the atomic size) precision
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This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing
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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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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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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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their application materials which of the three projects they are most interested in working on. About you You are working toward a doctorate in a social science discipline (or a relevant data science field), have
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October 2026 start ONLY For January and April starts please use the relevant application. This form is only to be used by those self-funded applicants seeking a place on a research degree programme at
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. Initial Application Portal: https://warwick.ac.uk/fac/sci/ceft/application-process/form Full Programme Admission Instruction Page: https://warwick.ac.uk/fac/sci/ceft/application-process/ Funding Notes
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Cranfield University's tailored researcher training programme – setting you up for a rewarding career in water science and environmental technology. Per- and polyfluoroalkyl substances (PFAS), also known as