75 parallel-computing-numerical-methods "https:" PhD positions at University of Nottingham
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learning, control theory, and embodied autonomous systems. The successful candidate will contribute to the development of learning-based control methods that are not only high-performing, but also safe
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spray drying are typically reliant on trial-and-error workflows, a narrow selection of polymers, and analytical methods that are unsuitable for early-stage screening due to high material demands. Moreover
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reliant on trial-and-error workflows, a narrow selection of polymers, and analytical methods that are unsuitable for early-stage screening due to high material demands. Moreover, no reliable predictive
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The rapid growth of deep learning has come at an extraordinary environmental and computational cost, yet the standard training paradigm remains remarkably unchanged. Every sample is passed through
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. What you should have: A 1st degree in physics or engineering. An interest in optics, some ability in computer programming A desire to learn new skills in complementary disciplines. You will work jointly
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facilities. A popular method of doing so is to create vast battery storage facilities which require incredible sums of upfront capital and rely on lithium, one of (if not) the most intensively mined resources
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for sustainable energy. Right now, there’s no fast, reliable method to predict how novel feedstocks split into valuable products like biochar, bioliquids, and gases. This PhD gives you the chance to change that and
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, mathematics, or related scientific disciplines. Skills in numerical tools and programming are desirable (MATLAB, python, C++ etc). Any experience or capabilities in engineering design or manufacturing methods
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engineering excellence needed for the aerospace sector. In this PhD, high-fidelity two-phase Computational Fluid Dynamics (CFD) methods will be used to model complex and fundamental cryogenic hydrogen flows
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data-driven methods to develop an inverse design framework for manufacturing systems. Together, we will advance the capability to design manufacturing systems that embed reliability, resilience