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to £20,780 plus fees for three years. The project brings together expertise in artificial intelligence and logistics and supply chain management, investigating the potential of adapting agentic AI to enhance
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/learning based techniques in the areas of robotics, or autonomous systems, • interested in autonomous systems and signal processing, • Keen to work with equipment and embedded
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-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
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intelligence, multi-agent systems, and the design of AI models. They will also acquire transferable skills in interdisciplinary problem-solving and innovation, which will significantly enhance
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zone in a very complex manner and lead the modelling to an imperfect zone of assumptions. These complexities allow the researchers to use approximations for useful lifetime calculations. Based
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can be costly, energy-intensive, and increase carbon emissions. Nature-based solutions (NbS), such as integrated constructed wetlands (ICWs), offer sustainable alternatives that combine effective
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project will develop novel methods for modelling and controlling large space structures (LSSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. Working with leading
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the molten pool. However, these models are computationally intensive and impractical for widespread simulations of large-scale part deposition. This project aims to develop a novel FEA-based approach
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the computational inefficiencies of physics-based models and enabling faster, potentially more accurate predictions. However, AI models require substantial volumes of high-quality, labelled training data, which
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transfer modelling, stochastic prediction of fungal growth and Ochratoxin A risk, and optimisation of in situ robotic sensing technology. Through this systems-based approach, the research will generate