63 computer-programmer-"Diamond-Light-Source"-"Diamond-Light-Source" positions at Cranfield University
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of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree
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to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development
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knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of
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. The integration of AI into hardware not only enhances performance but also reduces energy consumption, addressing the growing demand for sustainable and efficient computing solutions. This PhD project delves
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via joint activities with EU and UK cybersecurity hubs, preparing you for careers in trusted electronics, AI security, and national critical infrastructure protection. Graduates from this programme will
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supervisor/s within the theme are recognised. This is a 12 month, 100% research programme where you will achieve your MSc degree by developing a desk/laboratory/field based novel research project. Our current
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, highlighting its originality and significance. Outlines the methodology you propose to use to carry out your research, including the data sources and source material you plan to use. Suggests what you hope
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processing and storage, and supporting compliance with stringent certification requirements. Cranfield University offers a distinctive research environment renowned for its world-class programmes, cutting-edge
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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relevance. A digital twin framework for safe, simulation-based validation before deployment in operational wind farms. Develop explainable AI (XAI) frameworks and human-computer interfaces that enable wind