43 embedded-system-"https:"-"https:"-"https:"-"UCL" PhD positions at Cranfield University
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This PhD opportunity at Cranfield University invites ambitious candidates to explore the frontier of energy-efficient intelligent systems by embedding AI into low-power, long-life hardware platforms
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This PhD opportunity at Cranfield University invites candidates to pioneer research in embedding AI into electronic hardware to enhance security and trustworthiness in safety-critical systems
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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This PhD project will focus on developing, evaluating, and demonstrating a framework of novel hybrid prognostics solution for selected system use case (e.g. clogging filter, linear actuator, lithium
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/learning based techniques in the areas of robotics, or autonomous systems, • interested in autonomous systems and signal processing, • Keen to work with equipment and embedded
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Due to unique properties of supercritical CO2 (sCO2), power generation systems using sCO2 as working fluid have many advantages over their counterparts, such as gas turbines and steam turbine power
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This PhD opportunity at Cranfield University explores how next-generation AI models can be embedded within resource-constrained electronic systems to enable intelligent, real-time performance
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mitigating jamming and spoofing threats in real-time. Integration of Trusted Execution Environments (TEEs): Investigate the use of TEEs to create secure zones within embedded systems, facilitating secure data
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This project aims to bridge the gap between technological advancements and their integration into societal and environmental systems by shifting from a product-centric to a service-oriented approach
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A funded PhD studentship is available within the Autonomous and Cyber Physical Systems Centre at Cranfield University, Bedfordshire, UK. As aerospace platforms go through their service life, gradual