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analyses. Work iteratively with experimental colleagues to guide measurement design, interpret results and perform model validation. Contribute to reproducible modelling workflows (version control
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable
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modelling tools to real-world challenges in antibody engineering. The position offers the opportunity to contribute to the development of new technologies for precise control of antibody glycosylation, with
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respond to sound in ways that influence physiology, root function and pollination. This project will combine experiments, analysis and multiphysics modelling to establish credible mechanisms, limits and
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-ground processes and pollination. The research combines controlled experiments, advanced measurement and multiphysics modelling, and will generate open datasets and workflows to catalyse the emerging field
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as part of an international team examining the role of a breadth of ocean turbulence phenomena in controlling Antarctic sea ice extent, and the ways in which sea ice processes shape ocean
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may play in stress responses, below-ground processes and pollination. The research combines controlled experiments, advanced measurement and multiphysics modelling, and will generate open datasets and
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–dielectric–metal resonator platforms. The project develops hyperuniform disordered metasurfaces, a novel class of photonic structures in which structural correlations are precisely controlled to achieve
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device performance, reduce yield, and drive up production costs. Addressing this challenge requires new modelling approaches that can capture the full complexity of fabrication processes and enable
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of installation. Your work will include the development of advanced numerical models of the transition system, alongside the testing of a scaled prototype structure to demonstrate proof of concept. You will lead