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Field
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mangament in numerical models, including advanced calibration strategies from data (observations, measurements, other model predictions) and uncertainty reduction. Scientific context Many engineering and
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promising technology for producing large and complex metal component. Although its potential has been widely demonstrated, significant challenges remain in optimizing the process to ensure the quality
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, specifically methods that combine machine learning and optimization with physics-based simulation and/or physical constraints and translate these methods into impactful industrial applications. The position is
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provide detailed information on local deformation mechanisms at the microscale, while numerical simulations and data-driven approaches will enable the development of predictive models capable of linking
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the VILLUM FONDEN. The overall aim of the project is to introduce microstructural engineering to the field of additive manufacturing (AM) of metals. This is to set the stage for optimizing metals
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. Propose, develop, and test enhancements (e.g., shock-capturing strategies or improved numerical schemes). 3. Idealized supersonic flow simulations: Using the optimized code, conduct idealized simulations
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detailed information on local deformation mechanisms at the microscale, while numerical simulations and data-driven approaches will enable the development of predictive models capable of linking
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with the physical principles of structural dynamics and (vibro-)acoustics and the related numerical modeling techniques, such as the Finite Element Method (FEM), as well as numerical optimization
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modeling to create a predictive tool that spans orders of magnitude in length and time. Hands-On Numerical Modeling: Implement your model in a custom-made data analysis tool that uses advanced optimization
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of the position. The successful candidate will have a solid theoretical foundation in one or more of the topics: Computational Mechanics, Finite Element Analysis (FEA), Numerical Optimization