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Field
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical
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internships An additional disability allowance can be provided where appropriate Students opting to study part-time will receive a pro-rata maintenance stipend. Specified use +3 - covering a full PhD programme
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information from high-quality videos that share content with distorted footage as constraints in the learning process of modelling algorithms. This method uses the characteristics and knowledge embedded in high
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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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Exciting Fully Funded PhD: Computational Modelling for High-Pressure, Low-Carbon Storage Technologies. Be a Key Player in Shaping the Future of Clean Energy Storage! School of Mechanical, Aerospace
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PhD Studentship: Rolls-Royce Sponsored PhD Scholarship – Micromechanics and In-Depth Materials Analysis of Advanced Aerospace Materials Upon the Manufacturing Process Engineering Applications
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knowledge co-evolution and addressing complex challenges in a super-intelligent society. This project is situated within the rapidly evolving field of Cyber-Physical-Social Systems (CPSS), which is of
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with engineering, physics, mathematics, acoustics, fluids, electronics or instrumentation background. Prior experience in computational modelling is beneficial, but not mandatory. Similarly, experience
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Applications are invited to undertake a three-year PhD programme in partnership with industry to address key challenges in manufacturing engineering. The successful candidate will be based
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to achieve efficient and proactive reconstruction of the printing process, enabling real-time in-situ monitoring of large-volume material deposition and 2) How to adaptively compensate for size-induced defect