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positions in the interdisciplinary research environment “Culture and Society” to develop research on themes relevant to the thematic area “Digital Society, Infrastructure, Legacies”. This thematic area
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the mechanical properties of conjugated polymers and on the development of spinning processes for the manufacture of conducting polymer fibers for wearable electronics. A summary of the research field can be found
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understanding of electrolyte performance from molecular to device-relevant length scales. The main task for the doctoral candidate will be to develop and apply computational models to quantify and predict key
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interdisciplinary division within a larger unit together with Sociology and Social Anthropology, located at the Faculty of Social Sciences. The division is a well-developed research environment with clear roots in
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engineering programs and master's programs. More information is available on our website. Duties Applications are sought from highly motivated PhD candidates to work in a project aimed at developing sustainable
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for science, creativity and development. Therefore, we welcome applications from people with diverse backgrounds and perspectives. Where to apply Website https://web103.reachmee.com/ext/I017/1114/main?site=6
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usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and algorithms for resource-efficient
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(LTU) is one of Europe’s leading academic environments for mining-related research. To ensure continued development within critical raw materials research, we are now seeking a PhD student in Ore Geology
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translational research, research strategies and development, as well as education on both undergraduate and postgraduate levels. Where to apply Website https://lu.varbi.com/en/what:job/jobID:894214/type:job/where
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning