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This is an exciting fully-funded studentship (with a stipend of £18,800 p.a. plus full tuition covered for a 3 year period) under the ‘Cranfield Industrial Partnership PhD Scholarships Scheme’ (CIPPS). This PhD is designed to investigate a lighter, next generation alternative material to reduce...
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Fully funded Ph.D. opportunity in Aerospace AI. Sponsored by EPSRC and BAE Systems covering tuition, fees and a bursary of up to £19,569 (tax free). Combinatory Artificial Intelligence (also known
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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 those eager to pioneer AI-integrated hardware and contribute to next-generation intelligent systems. AI and ML are revolutionizing electronic system design, optimization, and security, fueling innovations
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a robust and safe dynamic hopping. The problem will be formulated in an optimal control framework which will consider all challenges and provide an optimised solution which can be implemented in real
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-uniform spatio-temporal risk allocation for each agent and 2) investigate the idea of optimal dynamic reconfiguration of the swarm’s topology to satisfy the obtained risk allocation. The resulting PhD
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, and more efficient operations. After all, the greenest energy is the one that’s not spent – and this project aims to unlock just that by refining the way we design and optimize airfoils. The focus
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that values equity, diversity, and inclusion, gaining unique expertise in aerospace systems design and integration (airframe, engine, subsystems), system of systems optimization, multi-fidelity models
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through advanced modelling and simulation. A key objective is to validate and optimize poroelastic finite element models of brain tissue, making them more accurate and clinically relevant. Additionally
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sources compared with gas turbines, etc. The aim of this PhD research is to develop novel performance simulation capabilities to support the analysis and optimization for sCO2 power generation systems