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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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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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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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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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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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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 the work. As a Research Assistant you will contribute to the research activities of the Centre for Autonomous and Cyber-physical Systems, especially concerning the specific projects described above
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to): Opportunity to speak with supervisors about the project and process; Opportunity to speak with contacts at Cranfield University and/or The National Archives regarding institutional support systems (eg