127 parallel-computing-numerical-methods-"Simons-Foundation" Postdoctoral positions at University of Oxford
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atmospheric physics, meteorology, climate, numerical methods, and data science. The Research Associate will be proficient in programming/scripting (e.g., in Python, and/or R, and/or Matlab, and/or Bash script
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and leading a programme of numerical simulations relating to all aspects of our research on P-MoPAs; using particle-in-cell computer codes hosted on local and national high-performance computing
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understanding of dark energy. Projects may span a broad range of topics, including improving Type Ia supernova modelling and standardization, developing and applying advanced data analysis and statistical methods
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- Develop original numerical methods for facility simulation in presence of expansion waves - Demonstrate improved estimates of rate constants for two-temperature models - Contribute
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effects. You will contribute to the numerical modelling part of the project, which will benefit from novel element level and centrifuge testing experimental results. You will set up and validate numerical
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of collaborative projects, working closely with clinicians, imaging experts, and computational scientists across the Oxford–Novartis Collaboration for AI in Medicine. You must hold a PhD/DPhil in Statistics
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to enable robust robot autonomy in complex, real-world environments. The post sits within our EPSRC Programme Grant in Embodied Intelligence and will advance the state of the art in localisation and scene
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of computational biology, molecular biophysics, and cutting-edge analytical technologies. You’ll contribute to the development and application of computational methods to understand protein folding, structure, and
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Colorectal Cancer - Stratification of Therapies through Adaptive Responses (CRC-STARS) programme, developing and applying cutting-edge mathematical methods to spatial transcriptomics imaging data in order to
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health. Specifically, our approach combines finite element modelling and medical image analysis. Our finite element brain models are based on tissue segmentation and our numerical simulations are validated