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speculative methods to explore how teacher education might take shape in an increasingly AI-integrated future. The work also involves examining today’s taken-for-granted assumptions and highlighting alternative
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is a risk that energetic interactions between hot corium melt and water coolant can damage the containment. The goal of this project is to develop methods for addressing the most influential sources
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accordingly. To connect local material properties to macroscale asphalt pavement performance, you will employ multi-scale numerical modelling based on the Finite Element method. This project is a collaboration
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of the Wallenberg AI, Autonomous Systems and Software Program and in Digital Futures . We have an extensive academic network and collaborate with researchers at MIT, UIUC, EPFL, among others. For more information
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isostatic pressing (HIP) and additive manufacturing. These methods must be tested for both corrosion protection and mechanical performance. This PhD-project will be performed in close collaboration with
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Technology We invite applications for a fully funded WASP-PhD position to join the new research group of Martin Trapp to work on the reliability and trustworthiness of machine learning models. You will work
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transformation, and AI-supported production, as well as industrial partners within XPRES and international networks. The postdoc will develop methods and tools for digital and model-based support in assembly
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and reinforcement. It will build on extensive pavement performance datasets and apply advanced machine learning and statistical methods to generate predictions and assess the effectiveness of different
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position within a Research Infrastructure? No Offer Description Project description Third-cycle subject: Architecture, Architectural Technology specialisation The PhD position addresses advanced design
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the group’s network. Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research