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oscillations and BSM processes. This will involve taking a lead role in developing dedicated software frameworks, including the implementation of machine learning techniques. Ultimately, the software will be
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theoretical physics, quantum computing, or machine learning and have completed or be in the final stages of a PhD in this or a related discipline. Main duties and responsibilities Design and analyse quantum
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A Machine Learning Enabled Physical Layer for 6G Radio Systems School of Electrical and Electronic Engineering PhD Research Project Directly Funded UK Students Prof Timothy O'Farrel, Prof Mohammed
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The design and testing of sounding rockets for hypersonic research School of Electrical and Electronic Engineering PhD Research Project Directly Funded UK Students Dr Alistair John Application
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Knowledge of workshop activity: for example vacuum technology, metrology, material finishing, CNC and conventional machining, welding (particularly TIG). Desirable Application/interview Experience with
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greener transportation and energy. Building on recent advances, the successful candidate will use a powerful combination of dynamical systems theory, optimisation, DNS and machine learning to model and
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, advanced statistical methods and the potential to develop pioneering reconstruction and calibration techniques involving machine learning. The PhD will prepare equally well for a career in industry and
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Machine Learning Approaches. You will have access to the excellent training opportunities at the University of Sheffield, and will spend time on site at Procter and Gamble. A range of highly desirable
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data gaps by combining process simulation (e.g., Aspen software) with machine learning techniques. By developing accurate, large-scale life cycle inventory data using enhanced digital tools like deep
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Hangsterfer's Laboratories) EPSRC Centre for Doctoral Training in Machining, Assembly, and Digital Engineering for Manufacturing PhD Research Project Directly Funded Students Worldwide Dr Thawhid Khan, Prof