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Field
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to develop an SDT that simulates real-time system operation in a dynamic virtual environment. 3. Online Implementation: Deploying the SDT on an evolving online platform, continuously updated with new data and
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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or in an academic role. We will help you develop into a dynamic, confident and highly competent researcher with wider transferable skills (communication, project management and leadership) with
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. For eligible students the studentship will cover Home or International tuition fees plus an annual tax-free stipend of at least £19,237 for 3.5 years full-time, or pro rata for part-time study. The student
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alongside New Zealand’s comprehensive landslide inventories to create innovative models of landslide behaviour. The research will combine field work, empirical modelling, and geospatial analysis, ultimately
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inculcate interdisciplinary working in our next generation of researchers. Interviews will be online in late August/ early September. Project Description: The increasing use of encryption in IoT
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their swimming dynamics and the mechanical deformations caused by the encapsulated active biomolecules, you will explore ways to control their motion in 3D space. Synthetic microswimmers have many potential
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Solubility, and dissolution dynamics, are key physico-chemical properties of active ingredient solid forms and are required across the chemical industry, however their determination remains a
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vulnerabilities. Frontier models show superior performance when combined with a focused knowledge base and multi-agent architectures. However, in most cases human involvement is still required, and fully autonomous
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require that applicants are under no restrictions regarding how long they can stay in the UK. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and