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connected large wind energy system dynamic modelling, control and analysis. In particular, the objective of this research programme is to lay the foundations of a new, model and methodology for Advanced wind
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multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a team with a strong track record in Big Data analytics in child mental health, helping accelerate the move toward
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relevant for hadron collider synchrotrons at the high-energy frontier, such as the Large Hadron Collider (LHC) and its High Luminosity upgrade (HL-LHC) at CERN, the European Laboratory for Particle Physics
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PhD in Computational Biology, Bioinformatics, Computer Science, Statistics, or a related quantitative field * Demonstrated expertise in programming and scripting (e.g. R, Python, Bash) for data
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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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develop the foundational and scalable tools, technology and methods needed to synthesise large sections of human genomes/chromosomes. Through programmable synthesis of genetic material the aim is to unlock
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relevant to setting a roadmap for ongoing experiments, as well as recently developed applications of tensor network techniques to large-scale partial differential equations. We are advertising two positions
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collaboration with colleagues in the John Radcliffe Hospital and the Oxford Big Data Institute, with the central aim being the development of rapid diagnostics of antimicrobial resistance in clinical samples. You
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Christopher Yau (http://cwcyau.github.io ) at the Big Data Institute, University of Oxford. This post will contribute to the development of a new simulation-based pre-training framework for building more robust