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conditions. This project aims to develop a new control framework that enables embodied decision-making in autonomous swarms, allowing them to operate with resilience, reliability, and adaptability. The work
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(£20,780 for 2025/26) and tuition fees will be paid. The offshore environment is characterised by complex and highly dynamic turbulent flows that define the performance and design of renewable energy systems
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paired with digital signatures provide confidentiality, integrity and authenticity. Nevertheless, the various functionalities offered by information technology services require more advanced protocols such
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to develop and analyse implementable, fully discrete methods for function approximation, density estimation, and/or time-dependent PDEs or SDEs in high dimensions, with links to UQ and theoretical
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(HVDC) technology will be used to bundle energy from several windfarms and transport to load centres. Future offshore wind farms are expected to be further optimized either functionally or in
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understanding of the underlying mechanisms, (2) optimise the process with online monitoring to ensure high consistency of quality, (3) use the knowledge gained to understand the opportunities and limitations
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to also improve and scale the process. We have made major contributions in this area, including the use of Machine learning to discover new cryoprotectants [Nature Communications 2024, 15, 8082
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, mechanical or chemical stability. The complex polymer formulations, multi-material components, and diverse use-case scenarios for such plastics create barriers that must be understood at the end of the product
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and emerging technologies can support the creation of garments tailored to diverse physical needs. Apply human-centred and experimental engineering approaches to evaluate adaptive garment performance in
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and signal processing methods using machine learning techniques to enhance the resilience, efficiency, and security of cell-free massive MIMO systems, which are expected to play a key role in next