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partners to reduce CO2 emissions in steel production using machine learning. You can find more information here . You will work on a theoretical and an applied project on data-enhanced physical reduced order
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towards a future-proof logistics system with a special focus on machine learning-based collaborative scheduling, resource sharing, and self-organisation. The EngD position corresponds to a 2-year post
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Model of Immersive Learning (CAMIL) by empirically examining how immersive VR supports learning processes across educational contexts. This position is part of a larger project funded by the Nationaal
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