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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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heavy software development component. The successful candidate will perform research in the application of machine learning (ML) techniques to the finite element method (FEM) in the context of composites
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heavy software development component. The successful candidate will perform research in the application of machine learning (ML) techniques to the finite element method (FEM) in the context of composites
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• Excellent command of English, both written and spoken. Desirable qualifications • Experience with battery development, electrochemical testing, and reliability assessment • Familiarity with finite element
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mathematics. An important aspect of the ongoing research is solving stochastic partial differential equations on surfaces, e.g., with surface finite element methods. The following requirements are mandatory: A
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collaboration with academics from the Bristol Composites Institute and Rolls-Royce PLC. This will entail introducing and validating finite-element based multiscale and multi-physics modelling methodologies and
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, computer science, or a closely related field. Coding experience for the computational modeling of physical and/or engineered systems, preferably with finite-element methods, is a must. Strong programming
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expertise in the analysis and design of concrete structures. Advanced proficiency in Finite Element Modelling (FEM) using tools such as Abaqus, ANSYS, RFEM, SAP2000, MIDAS or equivalent. Solid understanding
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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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research methodology is coupled CFD (Computational Fluid Dynamics) and FEM (Finite Element Method) modelling and simulations. This is the only methodology allowing simulations of fluid-structure interaction