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considerations Selection of suitable analysis models for SMART designs, aligned with chosen estimands and assumptions. Comparison of model performance (under violated assumptions, with respect to bias and loss
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the complex multiscale nonlinear interactions at the origin of such extreme events. In this project, you will develop machine learning-based reduced-order models which can accurately forecast
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. Successful applicants will investigate the relationships between processing, microstructure, and properties of metals through combined macro- and micro-mechanical experimentation and finite-element modelling
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of the fluids under consideration. This will be coupled with the use of in-house models that can be employed to explore and predict the behaviour of newly developed fluids in different components and applications
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constituents; manufacture and characterisation of the structural supercapacitors and batteries; test method development and post-failure analysis of the tested cells and; development of multifunctional modelling
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bottlenecks and challenges around stakeholder acceptance. The successful applicant will develop novel power system planning models that explicitly incorporate supply chain constraints and stakeholder