12 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Bournemouth-University" positions at The University of Manchester
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-driven AI models that capture the underlying process–structure–property relationships governing metal additive manufacturing. By combining mechanistic modelling, in-situ sensing, and machine learning
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for translational biocatalysis, addressing critical needs in the development of sustainable biotechnologies. The programme will equip PhD students with advanced expertise in enzyme science, machine learning, enzyme
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they can reliably, affordably, and fairly support a net-zero energy system. The research will focus on how data-driven and machine-learning-based control can coordinate demand, storage, and local generation
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, stiffness loss, damage evolution, and transient creep interact under coupled loading. The project will develop temperature-dependent constitutive models informed by numerical simulation. Machine learning
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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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group 14/15-element bond formation. In addition to the wide range of transferable skills developed during a PhD, the appointed researcher will learn and use: (i) Schlenk and glove box techniques; (ii
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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 acquire strong transferable skills (e.g. science communication skills developed by presenting results in group meetings and at national/international research conferences). Applicants should have or expect
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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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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