8 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"University-of-Chester" PhD positions at The University of Manchester
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aspects of machine learning. Applications include improving the efficiency of data assimilation methods and understanding why and how deep learning works. Applicants should have, or expect to achieve
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sluggish diffusion kinetics of HEAs make them excellent candidates for resisting oxidation and corrosion in high-temperature steam. Guided by thermodynamic modelling and machine learning, we will identify
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reusable plaque–flow atlas. Key objectives include to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric
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to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric archetypes (e.g. curved vessels, bifurcations, side branches
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overseas. Training can be provided in computational fluid dynamics, machine learning, and nonlinear dynamics. These skills are highly valued across a wide range of industries. Recent data reveals that Fluid
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to national priorities on sustainable agriculture and Net Zero targets. Furthermore, this project will enable the student to acquire skills in data analysis and visualisation, experimental planning, working
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characterization, computational modelling, or metal forming is advantageous but not essential. Enthusiasm and willingness to learn are more important. You will gain hands-on experience with cutting-edge experimental
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students per year across a number of UK institutions. The Skills and Training Hub will run online and face-to-face activities to facilitate cohort building and group learning exercises throughout the PhD