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spaces and habits for them. This is a highly interdisciplinary project that combines computational modelling and behavioural science. The first part will be based on the use of state-of-the-art
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MRI, echocardiography, and CT. Another promising approach is the use of cardiac digital twins—mathematical models that simulate a patient’s heart to allow the design and in silico testing of novel
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Modern numerical simulation of spray break-up for gas turbine atomisation applications relies heavily upon the use of primary atomisation models, which predict drop size and position based upon
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MRI, echocardiography, and CT. Another promising approach is the use of cardiac digital twins—mathematical models that simulate a patient’s heart to allow the design and in silico testing of novel
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, a state-of-the-art process-based model for groundwater risk assessment and contaminant transport modeling. By improving predictive modeling of transient contaminant source terms, this research will
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used to measure motion and deformation. It provides comprehensive full-field deformation data, essential for analysing complex materials, structures and model validation. The DIC community has developed
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noise models, leading to metrics devoid of assumptions about noise impacts (e.g., cross-talk or non-Markovian noise in gate fidelities). As shown by the supervisory team, non-Markovian noise can be a
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trends to provide immediate post-race feedback to Sport Directors that can be used to assess race strategy and tactics. Research, review and develop models based on objectives 1 and 2 to develop a race
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Biology, Physics, Applied Mathematics, Computer Science, Bioengineering, Systems Biology or a related field. Proficiency in modelling using differential equations is required. Candidates must have
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mathematics is essential. Prior experience with simulation tools or microstructural modelling is desirable. To apply, please contact the supervisor, Prof Andrey Jivkov - andrey.jivkov@manchester.ac.uk . Please