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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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of whom are PhD students. Within the research group for Energy Management and Planning, we conduct research and education focused on the development of efficient and environmentally friendly energy systems
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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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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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methodology manuscripts, developing/maintaining software packages, teaching, supervision and other academic activities. Qualifications Requirements for the position are: PhD or equivalent foreign degree within
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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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academic activities. Qualifications Requirements for the position are: PhD or equivalent foreign degree within Bioinformatics, statistics, genetics, or equivalent. The PhD must be completed no later than
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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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empirical and computational models to understand social learning. You will have the opportunity to develop your own research ideas under supervision. Your responsibilities include designing and conducting
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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