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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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, 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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, 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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considerably heavy in terms of computational resources and complexities. The common techniques for reducing computational resources include, for example, solving ordinary differential equations (ODEs) in
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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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techniques that are useful for the modelling of many real-life systems. These include the development and analysis of stochastic models, computer simulations, differential equations, statistical inference
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given to computationally intensive components, including ordinary and stochastic differential equations, and non-parametric components like Gaussian Processes. The project builds on the following key
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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