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improved accuracy for unsteady flow problems. Their principal strength is the lower numerical dispersion and dissipation they introduce compared with the low-order finite volume and finite element schemes
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, finite element analysis, programming and numerical methods. Applicants are expected to have achieved a First class (or, in special cases, an upper-class, 2:1) honours MEng/MSc degree or equivalent in
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the PhD, you will gain expertise in finite element modelling, electronic control and instrumentation, machine learning, experimental methods, and advanced signal processing. You will also build strong
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. Their principal strength is the lower numerical dispersion and dissipation they introduce compared with the low-order finite volume and finite element schemes that currently dominate industrial solvers. In
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. Experience with numerical methods, finite element method, statistics and machine learning is desirable. How to apply: Stage 1: Submit your 2-page curriculum vitae (CV), transcripts and a 300-word statement
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loading conditions. By generating datasets from finite element simulations, ML models can learn the mapping between unit cell design parameters and homogenised properties. State-of-the-art approaches
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candidate will enjoy working on finite-element based modelling, the application of mathematical concepts from UQ/ML to practical problems, and an understanding of scripting/programming. Individuals with
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proficiency in three of the items in the following list: - Fluent programmer (e.g., python or other) - Fundamental of finite element analysis and experience with FEA software
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REF2029 impact. Methods and workplan Data and simulation: curate multi-fidelity datasets that couple finite-element heat simulations with active thermography experiments. Model design: extend FNOs with
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experience in a range of industrially relevant computational engineering techniques. You will develop expertise in high-order finite element methods, mesh adaptation techniques, advanced parallel programming