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The Particle Physics group of the University of Bristol (tinyurl.com/particle-bristol ) UK, seeks a Research Associate or Senior Research Associate to join its machine learning R&D team
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. This project aims to develop machine learning models to predict a particle shape and size for a given chemical formulae and crystallisation method. Extractive Language learning models developed will be able
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, investment casting processing, computational modelling of casting processes at a macro-scale / component level and at a dendritic growth level, and machine learning methods for process optimisation. The ARCANE
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interdisciplinary researchers to grow ideas from concept through to manufacture, instrumentation, and testing, alongside advanced computational mechanics, machine learning, and data analytics workflows. For more
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. This project will, for the first time, incorporate nanoscale flow physics within a Brownian simulator of particle motion in channels. In this largely unexplored space we can expect that a complex interplay of
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of particle physics (e.g., the existence of dark matter in our universe, or the so-called “g-2” anomaly) will be explored using LHC Run-2 and Run-3 data using the golden “multileptonic signatures”, whereby
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required to induce unstable growth, the natural frequency and oscillation dynamics under ultrasonic irradiation, and their jetting collapse dynamics. You will learn how to set up, run, and post-process large
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single-mode optical fibre. Investigation of machine learning tools for micro-optics design. If you have an interest in photonics, quantum technology, and computer-based modelling, you would be highly
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of particle physics (e.g., the existence of dark matter in our universe, or the so-called “g-2” anomaly) will be explored using LHC Run-2 and Run-3 data using the golden “multileptonic signatures”, whereby
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would affect particle dispersion. • Share the new knowledge through publications with the wider scientific community. What will you Learn The main objectives of this project are: • Characterisation