104 security-"https:"-"https:"-"https:"-"https:"-"DFG-TRR" PhD positions in United Kingdom
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develop approaches to enhance security and resilience, helping to build trust in AI-enabled retail investment ecosystems. Funding Both Home and International students can apply. Only home tuition fees will
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Research themes: Cyber Security, AI, Decentralized Systems, Applied Cryptography UK students This 3.5-year PhD studentship is open to Home (UK) applicants. The successful candidate will receive an
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PhD studentship in Computer Science: From Formal Requirements to Specification-based Automated Testing for Safety-Critical Medical Device Software Certification Award Summary 100% fees covered, and
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Discipline: Engineering & Technology, Materials Science, Mechanical Engineering Qualification: Doctor of Philosophy in Engineering (PhD) This project is a collaborative research effort between Jaguar Land Rover and WMG (University of Warwick). The collaboration seeks to investigate and develop...
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PhD project: Modelling Reliability and Resilience of Hydrogen Systems for Improved Safety and Sustainability Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering
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: Imagine a surgeon operating remotely through a robot—what if the network slows at a critical moment? Even tiny delays can risk patient safety. This PhD project develops new AI approaches to predict network
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energy demand. Combining applied mycology, food safety modelling, precision agriculture and Net Zero energy systems, the research will deliver energy-efficient, data-driven grain storage solutions
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, allowing surgeons to operate on patients from different locations. However, communication delay remains a major safety challenge. Even small or unexpected delays can affect robot responsiveness and
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role involves designing and integrating real-time software algorithms with robotic hardware, including perception, control, communication, and safety modules to enable safe, precise, and reliable remote
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pretrained models, where parameter-efficient methods must balance erasure guarantees against model utility Unlearning as an AI safety primitive — exploring how federated unlearning can contribute to removing