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Field
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composites To propagate uncertainty in material behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help
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collaborates with prestigious global companies and institutions. This PhD project, a collaboration between AMADE and Btechc, addresses the high computational cost of modelling failure in large-scale composite
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candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context
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compositions. Initially, supervised learning models such as random forests, gradient boosting, and neural networks will be used to predict composition outcomes based on both literature scrapping and in-house
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international conferences. This project is ideal for students interested in advanced composite materials, structural/solid mechanics, sustainable structures, and material modelling. It provides a balanced
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The SCS department at UT's Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS) advances semantic modelling, cybersecurity, and service design to enable meaningful computing
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EM sensing. You will design innovative sensing systems and advanced data-processing methods to evaluate cutting-edge materials and structures, including carbon-fibre composites and complex