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on high-fidelity modelling and test data for both metals and thermo-set composite materials. To achieve this we will explore the use of advanced genetic algorithms and/or Artificial Intelligence (AI
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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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before the deadline. Computational haemodynamic modelling provides a powerful framework for linking blood flow dynamics with cardiovascular disease, using in silico approaches to systematically study flow
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powerful framework for linking blood flow dynamics with cardiovascular disease, using in silico approaches to systematically study flow environments associated with vascular health and pathology
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formed during late-stage deglaciation and subsequent marine transgression. These data will provide critical constraints for palaeoclimatic reconstructions and help quantify the magnitude and style
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stable isotope probing linked to multi-omics analyses. These system-level measurements will be integrated with analysis of the metabolic potential of our experimental communities using strain-resolved
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development, the studentship will provide exposure to the challenges and opportunities associated with translation of long-acting technologies towards clinical implementation. This studentship is closely linked
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, such links have predominantly been directly from a wind farm to shore (point-to-point). However, proposals are developing for multi-terminal grids for improved reliability and asset utilisation. Proposals
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developing better models for fragmentation of metals that include a consideration of the structure at the micro-scale, linking this to fragment formation at the macro-level. This will build on work in crystal
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University Belfast, University of Manchester, University of Edinburgh and University of Bristol. BioAID will train the next generation of scientists in Artificial Intelligence and data-driven approaches