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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
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their reliable operation, stagnating progress in scientific computing. While quantum effects threaten the continued scaling of classical computing, quantum computers are designed to exploit these effects
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implementing engineering wake models in WRF or similar activities. Production data from simulations will be compared with grid data for validation. She/he will closely work with industry and policy makers
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. Your work will feed directly into the development of predictive models that link microstructure to performance, guiding the design of alloys that are stronger, more reliable, and more efficient. By doing
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this fundamental challenge, the PhD candidate will be part of a wider team to establish methodological framework, combing utilisation of controlled tree growth test, thermodynamic modelling and advanced optical
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. • Opportunity to contribute to open-source verification tools with real-world impact. • Industry partnerships with organizations developing safety-critical AI systems. Work Location(s) Number of offers