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on the use of new Lyapunov-based deep learning methods. Such development includes: ideation, mathematical development, Lyapunov-based analysis, executing simulations and experiments, and disseminating research
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emphasizes innovation through the development of a Virtual Learning Environment (VLE) for construction safety or disaster response simulation, integration of Information Backbones including Digital Twins and
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for CI, HTI, and urban environments. The network emphasizes innovation through the development of a Virtual Learning Environment (VLE) for construction safety or disaster response simulation, integration
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apparatus equipped with thermocouples and thermal imaging to simulate realistic runaway events. Top-performing coatings will be validated in situ on live EV cells under controlled runaway conditions. Dr
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profile: Must-have: MSc or equivalent degree in computational simulations and related fields Theoretical knowledge of Molecular Dynamics and/or Monte Carlo Good English language – spoken and written Nice
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CV with full contact details, education/previous degrees obtained, prior work/research experience, and list of possible publications and theses with links to their online versions (if available) copy
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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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response timelines. Building on this foundation, the project will apply scenario modelling and simulation techniques to investigate emergency event propagation, routing strategies, vehicle-task assignment
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Application process To apply for the studentship please click the ‘Apply’ button above to complete the application form and email it with a copy of your CV, two academic references, a copy of passport photo
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affect ignition behaviour. You’ll use advanced tools such as chemical kinetic modelling, multi-dimensional CFD simulations, and collaborate closely with experimental researchers. You will receive