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hybrid discretisation frameworks that apply varying fidelity across the computational domain. This includes creating conservative transmission algorithms that ensure consistent exchange of information
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mathematics, statistics, or machine learning, or a closely related discipline • OR near to completion of a PhD • Expert knowledge of Bayesian computation and deep learning methods • Excellent
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based in the Department of Informatics within the Faculty of Natural, Mathematical & Engineering Sciences (NMES), an internationally recognised centre for research in robotics, artificial intelligence
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rigorous simulations and live demonstrations. This position is ideal for a researcher with a passion for solving complex problems at the intersection of wireless communications, edge computing, and machine
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extreme threats to national security. AWE has pioneered advancements in areas including physics, engineering, materials science, and high-performance computing. Together we’ve helped shape the UK’s
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. Understanding of research communication and impact. Excellent analytical skills. Ability to plan and work independently, taking responsibility for work in a proactive and independent manner. Ability to work
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to work within established research programmes. Applicants will have good quantitative skills and knowledge of statistics including experience of statistical programming software (e.g. R or Stata
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the discipline to work within established research programmes. Excellent communication skills, including the ability to write for publication, present research proposals and results, and represent the research
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to the development of research programmes and methodologies including online and in-person workshops and field work with teachers Ability to routinely communicate complex and conceptual ideas to those with limited
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Coupled Plasma) torch design for high enthalpy thermal plasma flow. Developing/Validating computational models for a dual stage plasma torch. You should hold a PhD (or equivalent experience) in Plasma