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. By integrating artificial intelligence (AI), multi-sensor fusion, and cognitive systems, the research will pioneer robust navigation architectures. These improvements are key to making future transport
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costs [1]-[5]. Building on these previous findings, this PhD project will design a new MARL architecture that incorporates model uncertainty, cyber-attack scenarios, and network reconfiguration events
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The Cambridge Sustainable Design Group within the Department of Architecture invites applications for the role of Research Assistant/Associate in Thermal Infrared Remote Sensing for Decarbonisation
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enhanced electrochemical sensing, field-responsive coatings, and energy-harvesting architectures. The candidate will use advanced fabrication, microscopy, and spectroscopy techniques, and work within an
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. By integrating artificial intelligence (AI), multi-sensor fusion, and cognitive systems, the research will pioneer robust navigation architectures. These improvements are key to making future transport
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methods that are data-efficient, reliable, and deployment-ready. Aims Advance operator-learning architectures for transient heat transfer in complex geometries, integrating physical priors (PDE structure
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, visions and concerns in innovation. Entry requirements The standard minimum entry requirement is 2:1 in Geography, Sociology, Political Science, Anthropology, Design, Architecture, Engineering, Computer
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, particularly in computer networks, operating systems, computer architecture and distributed systems Excellent programming, system building and measurement skills are required Be familiar with, and ideally worked
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. Candidate Requirements A strong academic background in Engineering, Mathematics, Physics, Architecture or Computer Science. An undergraduate degree with at least 2.1 in one of the above subjects is essential
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catalytic performance. Applications include enhanced electrochemical sensing, field-responsive coatings, and energy-harvesting architectures. The candidate will use advanced fabrication, microscopy, and