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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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dynamics resulting from varying compositions. An adaptive Model Predictive Control strategy is envisaged for this challenge. During the four-year doctoral programme, you will contribute to the group's R&D
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
Thermography. This raw dataset is needed to be processed and annotated to train supervised and unsupervised AI models. The research will aim to develop deep learning algorithms for damage classification
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mechanisms, namely the remodelling of membrane lipids. Lipid remodelling is a process whereby bacteria selectively modify their membrane lipid composition in response to a particular environmental stimulus
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variations in the chemical composition of the steel produced, which may affect the performance of the steels during processing and service. This is particularly true for stainless steels undergoing phase
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composites. Position 1 – Ply and blank bending during hot press forming Wrinkling is one of the primary process-induced defects in press-formed thermoplastic composite parts. Predicting wrinkling requires
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. This PhD will utilise thermodynamic modelling to obtain predictions of equilibrium and, where possible, non-equilibrium phases for a matrix of compositions covering flat rolled products including elevated
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frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data
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of the primary process-induced defects in press-formed thermoplastic composite parts. Predicting wrinkling requires accurate constitutive models for the bending behavior of molten reinforced thermoplastic plies
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optimisation The reliable production of EAF processed steels requires a robust understanding of the kinetics of microstructure formation and how they are influenced by compositional and processing variations. In