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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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is a technically challenging process due to the exponential number of variables that one has to account for, when making carbon footprint assessments for products and processes. The tools, methods
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Embarking on a pioneering journey, this project delves into enhancing real-time decision-making in evolving cyber-physical systems (eCPS), crucial in today's modern manufacturing landscape. Focused
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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to realise their full potential, from academic achievement to mental and physical wellbeing. We are committed to progressing the diversity and inclusion agenda, for example; gender diversity in Science
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systems by developing deep learning and physics-informed deep learning models. These models will calibrate raw pressure sensor data to capacitive sensor data and incorporate flight variables to improve
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from over 100 countries and support our staff and students to realise their full potential, from academic achievement to mental and physical wellbeing. We are committed to progressing the diversity and
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of water consumption and water pollution. Moreover, only 1% of the materials used in the production process of clothes are recycled back even though around 95% could be recycled. The industry is built
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and Cyber-Physical Systems Research Assistant in Human Augmentation with AI Fixed Term Contract until 14 March 2025 or for 9 months (whichever is sooner) Full time starting salary is normally in
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manufacturing and process or material science. About the Role This is an exciting opportunity for you to contribute to a new industry project funded by Airbus on the optimisation of next-generation landing gear