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collaborate with each other as well as with other PhD students and postdocs to create new knowledge and synergies across related research activities. The successful candidates will also assist in teaching at
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relevant to railway infrastructure design and safety. This position will be in the research group of Division of Soil- and -Rock Mechanics at the Department of Civil and Architectural Engineering. Key
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, Production and Design (PPD) have about 35 dedicated employees committed to advancing research and education in industrial product realization in close collaboration with industrial partners. Our workplace
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strategies to address them within the scope of the project Develop new agentic AI methods for the autonomous configuration and deployment of 6G core functions in the edge-cloud continuum Design, develop, and
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working time will be devoted to your own research but you will also be expected to co-supervise graduate students and collaborate with PhD-students. In addition, you may take part in other teaching
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of the obtained materials. The work duties also include studying water and moisture adsorption of the nanocomposites, as well as evaluating the performances of the designed electricity generation devices. You are
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aims to secure the network infrastructure of distributed AI. The focus is on designing resilient architectures and protocols that combine network-level protections with AI- and system-level adaptivity
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) for recent research and messages to prospective members. This postdoctoral project aims to secure the network infrastructure of distributed AI. The focus is on designing resilient architectures and protocols
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-reviewed international journals and at major conferences. The position will include supervision of PhD and MSc students, teaching and supporting in acquiring funds for future research projects from research
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for the autonomous configuration and deployment of 6G core functions in the edge-cloud continuum Design, develop, and use digital twin environments and validation tests to ensure correctness and reproducibility