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Field
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on HPC clusters for large-scale hierarchical tomography apply algorithms to experimental tomographic data collaborate closely with beamline scientists and project partners publish and present results in
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. RESPONSIBILITIES: Implementing laboratory and computational workflows for large-scale biological studies Collecting experimental and high-throughput data Characterisation of molecular and cellular mechanisms
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Zahtila . Project description Particle-laden flow is a ubiquitous phenomenon and relevant to many engineering processes, ranging from large-scale environmental and industrial transport to microscale
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communication is interrupted, such as during disaster recovery, in remote industrial sites, or for temporary large-scale events, fixed infrastructure is either unavailable or easily damaged, creating "blind spots
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qualification. Background knowledge and experience in engineering and computer science disciplines and in areas such as CFD post-processing (OpenFOAM), wind turbine renewable energy systems, big data management
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, Grobelnik M, Fuart F, Stopar L, Epelde G, Fischaber S, Poliwoda P, Rankin D, Wallace J, Black M, Bond R. Meaningful big data integration for a global COVID-19 strategy. IEEE Computational Intelligence
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control performance and efficiency. This PhD project focuses on data-driven analysis of confined liquids structure, informed by total neutron scattering. The emphasis is on developing new analysis
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Health emergencies, mass casualty events and disasters generate large volumes of operational, clinical and organisational data. However, these data are rarely synthesised into structured, analysable
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of habitat diversity in agricultural environments as a proxy for potential biodiversity. You will plan and conduct field measurements and perform data analysis in a large interdisciplinary project group
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that they can integrate it into their large-scale quantum computer system engineering models. SKILLS. Candidates must have a high-quality background in quantum information or quantum physics, and an