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to produce cutting-edge research. Prospective applicants must: Hold a good honours degree in Physics, Maths, Engineering, or a related discipline. Be familiar with differential equations and have some
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to produce cutting-edge research. Prospective applicants must: Hold a good honours degree in Physics, Maths, Engineering, or a related discipline. Be familiar with differential equations and have some
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, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will have experience in one or more of these subject
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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will
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Biology, Physics, Applied Mathematics, Computer Science, Bioengineering, Systems Biology or a related field. Proficiency in modelling using differential equations is required. Candidates must have
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Biology, Physics, Applied Mathematics, Computer Science, Bioengineering, Systems Biology or a related field. Proficiency in modelling using differential equations is required. Candidates must have
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, partial differential equations and scientific computing, to name a few. There are competing LC theories e.g., molecular-level models with molecular-level information, mean-field models that average
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model due to the mathematical challenge of solving the multiple partial differential equations simultaneously. With the support of the combined sponsorship from the university and industrial partner
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systems of differential equations. The resulting models will be analysed with analytical tools from applied mathematics and numerical studies in the Julia programming language. The successful candidate
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in numerical techniques used to solve ordinary and partial differential equations and be proficient with related commercial or open-source software tools (e.g., ANSYS, FEniCS, OpenFOAM or similar) and