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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a
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addresses subsurface uncertainties and evaluates commercial viability, promoting regional awareness and policy development for strategic alignment with regional and governmental priorities in relation to Net
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crystalline resins for use in two-photon polymerization. New forms of imaging hardware will be utilized in collaboration with partners to provide greater understanding of the polymer network morphology and how
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aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits
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understanding of the polymer network morphology and how it relates to the laser process parameters. Specifically, you will carry out high resolution Raman imaging on laser written polymer networks with liquid
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global extinction risks and climate impacts embedded within international wood trade networks, enabling robust attribution of environmental impacts from harvest to consumption. These will be used
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access to world-class imaging facilities and a vibrant interdisciplinary network. Informal enquiries are welcome and should be directed to Professor Betty Raman (betty.raman@cardiov.ox.ac.uk ). Only
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access to world-class imaging facilities and a vibrant interdisciplinary network. Informal enquiries are welcome and should be directed to Professor Betty Raman (betty.raman@cardiov.ox.ac.uk). Only
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networks. This position is part of a UK-Canada Quantum for Science collaborative project "Quantum network applications in theory and practice" funded by STFC/EPSRC (UK) and NSERC (Canada), led by Professor
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-established data resources accessed within a secure Trusted Research Environment for large-scale analysis. This role offers excellent opportunities to engage with a broad network of collaborators including