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should have for this project, in addition to our standard entry requirements . Essential Criteria BEng/BSc/MSc in Mechanical Engineering, Applied Mathematics, Applied Mechanics, or related discipline such
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, machine learning, and information-theoretic approaches to achieve robust, non-intrusive security for the ever-expanding IoT landscape. Feature Engineering for Encrypted Traffic: It is crucial to identify
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hardware-software prototype for Non-Intrusive Load Monitoring (NILM) that can provide real-time, interpretable energy-saving suggestions to households—completely on-device. This role involves applied machine
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. The developed technology will be rooted in causal reasoning, and we envision leveraging causal generative AI methods to develop a rigorous and comprehensive AI testbed. Clinical applications include, but are not
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, their validity often does not extend to the operating environment of a gas turbine, often neglecting important engine-realistic geometric features and transient operation. In this PhD project, you will apply
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science, along with proven skills in prototyping software using real-time 3D engines and implementing machine learning models. With 50+ researchers and PhD students, the Centre for Sustainable Cyber Security (CS2
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innovation through applied research, bridging academia and industry. Students will have access to state-of-the-art laboratories, hardware/software resources, and design facilities, supporting AI-powered
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, Cranfield fosters innovation through applied research, bridging academia and industry. Students will have access to state-of-the-art laboratories, hardware/software resources, and design facilities
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undergraduate degree in an Allied Health, Engineering, Biomedical or Sport Science or Biomechanics related field. An optional MSc including human biomechanics would be desirable. Candidates with experience in
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interface designs that enable real-time, efficient processing in resource-constrained environments. Students will explore innovations in hardware-software integration, emphasizing energy efficiency