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Field
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, teams are adapting to significant technological advances accompanied by great volumes of incoming data. Such changes have the potential to improve capabilities in complex and uncertain conditions, but
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science applications Computational Atomic-scale Materials Design with a focus on materials modeling and discovery with electronic structure calculations and machine learning Luminescence Physics and
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learning surrogates. In order to expand from the boundaries of the learning space, effectively generalize knowledge and extrapolate behaviour for unseen conditions, unseen locations and even unseen turbines
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microscopy. Root architectural phenotyping. Plant and root physiological measurements to determine the function of root phenotypes. Functional-structural plant modeling to determine the benefit of root
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prone to change and decline. One project component analyses and models the effects of climate-induced cryospheric changes on water flows. The other project component, to which you will contribute
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, in collaboration with Rolls-Royce, will develop innovative coatings to safely contain hydrogen in critical aerospace materials through experimental and computational modelling work. You’ll join a
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electron microscopy image simulations Development of a machine learning model capable of inferring 3D atomic structure from two-dimensional TEM projection images Application of the new approach
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state beyond a certain speed. Although predictions of sub-synchronous vibrations with current codes have shown good correlation with experiments under controlled lab conditions, this was only up to a
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instruments, data analysis as well as theoretical and numerical modelling provide excellent research opportunities for PhD students. Solar system science research subjects include cosmochemistry, small bodies
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and develop frameworks and knowledge on planning and coordination of resources within and across projects. apply quantitative methodologies, such as simulation and analytical modelling, to develop