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Field
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of the studentship is to measure and model the optical properties of facial skin at different wavelengths and angles and to relate the results to other work by the research team on the clinical measurement and
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motion and the viewing perspective of the observer (Nikolaidis et al, 2016). This project will develop continuous models of action legibility using these sources of information from data collected in a
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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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cultures—a powerful 3D ex vivo model—this project will dissect the mechanistic links between mTOR signalling, reactive glial phenotypes, and complement activation. The project will also incorporate human
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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process is poorly recorded and needs improvement. Aims and Objectives In collaboration with the Health Innovation Partnership, a modelling pipeline will be devised to cope with the challenges of data
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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The project: We invite applications for a fully funded PhD studentship in the Solid Mechanics Group at the University of Bristol to work on the predictive modeling of hydrogen-induced damage in
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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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