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We are seeking a motivated PhD candidate to work on an exciting project that will advance correlative microscopy and microanalysis methods for studying mineralised biological materials and
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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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teaching in model-based systems engineering from Loughborough Systems Engineering experts and industrial leaders. This PhD studentship is offered by the Model-Based Systems Engineering doctoral centre https
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the growing demand for sustainable AI-enabled systems, this PhD brings together low-power computing, energy-aware design, and thermal optimisation. You’ll work with advanced profiling tools, prototype long-life
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student with an aerospace, mechanical engineering, or physics background. Experience of experimental and computational modelling of icing physics, instrumentation and imaging techniques would be
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Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace, mechanical
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lives, appearing in processes such as in additive manufacturing, the Earth’s atmosphere, and inkjet/biomaterial printing. The key to understanding (and ultimately engineering) these applications is how
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electrical/mechanical engineering. Expertise in numerical electrical machine design tools (Ansys, JMAG, .etc) as well as corresponding scripting skills are desirable. Experience in electrical machine prototype
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per year for 3.5 years. Lead Supervisor’s full name & email address Dr Massimiliano Fasi: m.fasi@leeds.ac.uk Project summary The growing importance of artificial intelligence is fostering a paradigm
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for the company, which will open new markets and opportunities. This PhD covers a wide range of skills and so it is essential the candidate has a 1st degree in electronics, computing or aerospace/space technology