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discovering research and potentially pursuing a PhD. Expected skills • Solid background in numerical methods (PDEs, finite elements, scientific computing). • Interest in modeling, model order reduction, and
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automated simulation and analysis workflows 4. Developing a detailed mechanistic understanding of the interactions between oxidative and reductive processes in metal-ion batteries 5. Communicating scientific
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certification, dramatically accelerating innovation cycles. What you will gain: Expertise in Finite Element Analysis, Scientific Machine Learning, Uncertainty Quantification, and Professional Programming
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of finite element analysis and materials modelling Applicants with a good 2.1 degree are also encouraged to apply if they can demonstrate an excellent background in mechanics and/or modelling. Application
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engineering (CAE, e.g., Abaqus, Ansys) software tools commonly used in additive manufacturing, structural design and related research (e.g., parametric design tools, finite element simulation tools, path
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. The following skills are highly desirable but not essential: Ability to program in Matlab/Python Experience with Finite Element Analysis and Reduce Order Modelling Experience in Rapid Prototyping and CAD Design
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Mathematics, Applied Mechanics, or related disciplines (a minimum honours degree at UK first or upper second-class level) Experience in computational fluid dynamic/finite element modelling by using commercial
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department at TU/e for finite element-based deformable body simulations. Conduct research on mechanical contact processing models, integrating both physics-based numerical models and data-driven approaches
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knowledge of numerical methods - knowledge of free-surface flows, granular flows - proven experience in numerical simulation (finite elements, finite volumes, CFD, LES, etc.) - proficiency in basic scientific
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., finite-element analysis) of scorpion species. Exploring evolution and biodiversity of Australian scorpions through methods such as phylogenetic analysis, geometric morphometrics, distribution modelling