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. This unique position combines advanced finite element modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an
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modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an innovative startup partner. You will be
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of numerical modelling (e.g. CFD, FEA, FSI, optimization, ML), but we are also involved in experiments and real-life monitoring to support our findings. Besides research, our division is actively involved in
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environments ensure safe operations Our activities are primarily related to the development and application of numerical modelling (e.g. CFD, FEA, FSI, optimization, ML), but we are also involved in experiments
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with GKN Aerospace Sweden with partial work there and combines numerical and experimental research. Project overview The purpose of this project is to contribute to the development of an ultra-efficient
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and numerical modelling of natural clays at both laboratory and field scale. We are active members of the ALERT Geomaterials network and other international committees. Our diverse and international
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different mixing and reactive properties compared to conventional fuels. In this project, turbulent mixing and combustion of hydrogen in air will be studied through optical experiments and numerical modelling
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Implement advanced numerical concepts and material models to describe damage initiation and progression in a multi-phase microstructure Use the numerical framework to perform numerical studies generating new
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numerical modelling of natural clays at both laboratory and field scale. We are active members of the ALERT Geomaterials network and other international committees. Our diverse and international team of over
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Computational Arts, Music, and Games within the DSAI division. About the research project This position is related to investigating learned cultural representations in data search spaces of generative AI models