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stamps to prove a message’s origin and integrity, are key to secure communication. Falcon is a new, standardised digital signature scheme that remains secure even against quantum threats. It uses small
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Department/Location: Department of Engineering, Central Cambridge We are seeking a highly motivated Research Assistant/Associate to join EPSRC and industry funded Digital Roads (DR) Prosperity
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MRI, echocardiography, and CT. Another promising approach is the use of cardiac digital twins—mathematical models that simulate a patient’s heart to allow the design and in silico testing of novel
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
intelligent methods that integrate large language models (LLMs) and knowledge graphs to interpret technical documentation and structure complex engineering knowledge. The goal is to create digital twins
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roads. The post holder will be located in West Cambridge, UK. Key Responsibilities: Create proposals for standards, covering Digital Twin (DT) communication protocols for interfacing with different types
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. You will be able to manage your own research activities independently and will have contributed to academic publications or digital research outputs. You will have excellent communication skills and be
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scientific research on health Public policy relating to arts and health Community health and public wellbeing through the arts Cultural interventions to prevent illness and promote wellbeing The role of arts
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simulation study of light matter interaction, digital twin enabled process development and life cycle assessment will be researched. Opens: Immediately Deadline: 08/08/2025. Duration: 36 months Funding: Funded
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by the British Academy. Based in Bodleian Special Collections at the Weston Library, the successful applicant will contribute to the redevelopment of MLGB as a sustainable, open-access digital resource
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast