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Field
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chemistry. Working with us means learning something new every day. You will be affiliated with the research project, with the working title Q4-BIO, where we open a new field of research around quantum tools
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implement a hyperspectral imaging system tailored to bulk forensic trace analysis and develop chemometric and machine-learning models for material identification and classification. You will evaluate
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profiles. Identification of molecular signatures in platelet disorders and bleeding disorders of unknown cause (BDUC). Development of machine learning models for patient stratification. Integration
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. We are seeking a candidate motivated to explore the how emerging technologies – such as machine learning, generative AI, and extended reality (XR) – impact societal preparedness planning required
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they bond in materials, but also develop transferable skills in scientific computing, data analysis and visualisation. "Machine learning for atomic-scale structure determination in thick nanostructures" (with
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machine learning and conventional optimisation techniques. 2. To design and optimise magnonic primitives for wave-based neuromorphic computing, including programmable devices enabling nonlinear activation
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existing models struggle to capture this complex, multiscale phenomenon efficiently. This project will develop a novel, physics-informed surrogate model using Bayesian machine learning to predict gas
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and support well-founded decision-making within the programme. You design and organise engaging workshops and learning experiences for students, connecting digital and technological developments to real
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departments of this type in the country, with a particular strength in power engineering. The increasing use of liquid cooling in aerospace electrical machines introduces direct interaction between liquid
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aviation sector is undergoing a once-in-a-generation transformation towards cleaner, more sustainable flight. Cryogenically cooled electrical machines are of significant interest for the future of air