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demonstrated ability to communicate and interact with a diverse range of stakeholders and students. Demonstrated knowledge in Quasi-Monte Carlo methods and/or finite element analysis and/or machine learning is
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codes, finite element or finite different methods, peridynamics, phase field models, multi-objective optimisation methods, CAD. Demonstrated ability to adapt to fast-changing project direction and learn
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finite element methods, which demand extensive data and are costly, PINNs embed governing physical laws directly into the learning process. This allows effective management of limited and noisy data
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software, and applying computational methods and analysis tools, including finite element analysis (FEA), to optimise designs. Providing leadership and supervision to engineering staff, ensuring research
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outcomes of this research will be well-validated computational analysis methods to predict failure mode, and quantify defect and damage produced in fibre metal laminate composites. The models can be used
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techniques and associated tools (examples include, but are not limited to machine learning, density-functional-theory, materials informatics, finite-element modelling, phase-field modelling), and demonstrated
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) + Finite element methods for complex flows in porous media (generalized multiscale finite elements via autoencoders, adaptive in space and time, splitting methods, and variational flux recovery) + Adaptive r