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Field
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. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network
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. Our approach enhances T2 (Interconnected QC systems) through verification methods for connected networks, supports T1 (Integrated quantum demonstrators) via hardware-agnostic metrics, and enables T3
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each year. Modern applications—from power grids to vehicle platoons—depend on large networks of autonomous subsystems. Without a solid theoretical underpinning, ensuring both collective objectives
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include contributing to the developing programme of work on decision analytic modelling in cancer research, develop networks and links with collaborators, external contacts and leading thinkers in
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include contributing to the developing programme of work on decision analytic modelling in cancer research, develop networks and links with collaborators, external contacts and leading thinkers in
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emissions from transport. Decarbonising aviation is a vital part of achieving net zero. Hybrid and ‘all electric’ aircraft technologies offer a pathway to net zero. The electrification of aircraft, for both
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rate (£20,780 for 2025/26) and tuition fees will be paid. With the UK and global ambitions to achieve carbon net-zero by mid of the 21st century, carbon footprint (CF) management has become increasingly
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part of achieving net zero. Hybrid and ‘all electric’ aircraft technologies offer a pathway to net zero. The electrification of aircraft, for both onboard and propulsion systems, is the path forward
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-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique
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PhD Studentship available in the EPSRC Centre for Doctoral Training in Robotics and AI for Net Zero (RAINZ) This studentship is offered by the EPSRC Centre for Doctoral Training in Robotics and