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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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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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. 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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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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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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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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research team. Good knowledge and experience in heat and mass transfer is essential and proficiency in the use of Computational Fluid Dynamics will be considered an advantage. The student will benefit from