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, smart materials that combine memory and learning directly within their structure, uniting theory and experiment. These materials adapt their computations over time, reducing energy use and improving
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are looking for a highly motivated and skilled PhD researcher to work on structural surrogates of offshore wind foundations through graph-based machine learning. Our goal is to perform full-structure
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consistent scientific methods for mobility planning and management, (2) integrate a new set of modular metrics for responsible mobility, (3) embed the planning methods into the open data AgiMo Digital Twin, (4
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of systems and data sources. Using the DR research to create guidelines for the development of the ontological structure for the DT. Development of DT information modelling, data fusion, and forecasting
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consistent scientific methods for mobility planning and management, (2) integrate a new set of modular metrics for responsible mobility, (3) embed the planning methods into the open data AgiMo Digital Twin, (4
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, into simulation models used for health systems management. The research will provide practical and methodological contributions. The framework will offer healthcare decision-makers better tools for designing
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have the opportunity to build their research portfolio within a range of projects within Prof. Ruffini’s Research group. The researcher will also be part of the School of Computer Science and Statistics
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and transfer of science to society. As a modern employer, it offers attractive working conditions to all employees in teaching, research, technology and administration. The goal is to promote and
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Fully Funded PhD Research Studentship tax-free stipend of £20,870 Design, Informatics and Business Fully Funded PhD Research Studentship Project Title: Behaviour-Based Anomaly Detection
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industrial collaboration. DCs will benefit from close mentorship by leading researchers and industry experts, while also developing essential skills in scientific writing, research ethics, time management, and