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) approaches, along with Large Eddy Simulation, have demonstrated maturity in the prediction of many buoyancy-driven flows but require extensive validation. Two- and three-dimensional Computational Fluids
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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waters at scales not seen in the UK. The reduction of nitrogen loading is the primary driver; however, the NBS also have capacity to deliver parallel removal of phosphorus. The NBS include the restoration
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working with high performance computers (e.g., parallelizing and distributing code). Experience in distributed data management and workflow systems. Preferred Competencies Ability to work independently and
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research projects. In parallel, they participate in the comprehensive BIGS DrugS education programme, which includes workshops, lectures, colloquia and symposia. Mentoring is performed by two experienced
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in geophysics, physics, geoscience, computational geoscience, or related natural sciences with an overall grade of at least good Experience in programming (e.g., matlab, phyton, C/C++) and parallel
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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Are you interested in developing computational tools and learning strategies for understanding health and disease at the microscopic scale? Would you like to be part of a research team with skilled
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government’s Advanced Modular Reactor (AMR) programme has recently identified HTGRs as the preferred design for future advanced nuclear deployment in the UK, with an aim to deliver a demonstration reactor by the
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collaborative investigation including teams with expertise in cell biology, molecular biology, biomedical engineering, clinical medicine and computational biology. The team aim to create a novel, targeted