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ballot may leak the voter’s preferences if published publicly in plaintext, as the ranking order can reveal detailed information about the voter’s identity. Therefore encryption is necessary to hide
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for success: A PhD awarded in a relevant discipline e.g. social sciences, history, science and technology studies, anthropology, or other discipline with a strong background in conducting qualitative research
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Loughborough University Vice Chancellor’s PhD Cluster – Super-Smart Textiles (SuS-Tex) for Healthcare and Sports. The Doctoral Researcher will join a cohort of five researchers belonging to the SuS-Tex cluster
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37 Faculty of Business, Economics and Statistics Startdate: 01.11.2025 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 31.10.2029 Reference
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and is a leading multidisciplinary research-intensive technology university delivering economic, social and cultural benefits. For more information please visit: https://www.brunel.ac.uk/about/our
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health, and intelligent operations. It is particularly suited to candidates with academic backgrounds in Industrial Engineering, Aerospace Engineering, Management Science, Operations Research, Transport
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
aerospace environments. The objectives of the PhD are: •Extract structured engineering knowledge from unstructured maintenance data using LLMs, and represent it using ontologies and knowledge graphs •Develop
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’. Use ‘Course Search’ to identify your programme of study: · search for the ‘Course Title’ using the programme code: 8040F · Research Area: Civil Engineering · select ‘PhD Civil
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to pursue your own interests (subject to delivering the key laboratory, experimental goals of the projects you will be funded by). For more information about the School of Engineering at Newcastle University
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& robust control, and learning for dynamics & control. The main task of the PhD student will be to develop sound data-driven methodologies for learning control policies with provable guarantees