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Field
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for the rapid conversion of biomass into hard carbons, significantly reducing the time and energy required compared to traditional processes. Additionally, the collaborative research aims to identify optimal
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Innovative city . The position is formally based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science . At STIMA, we conduct research and
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Engineering (ISY), Linköping University. The Division for Communication Systems carries out research, undergraduate and postgraduate education in communications engineering, statistical signal processing
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Help for applicants . Application procedure Apply for the position by clicking the “Apply” button below. Your application must reach Linköping University no later than 16 of April 2026. Applications and
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research on raw materials, materials, processes, and systems to contribute to the sustainable development of forest resources and a circular bioeconomy. The department is located on campuses in both Umeå and
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properties and processes, as well as how soil functions are affected by changes in environmental conditions, including climate change. Through research, environmental analysis and education, we contribute
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strong interest for microbial soil processes and an ability to conduct field work in remote places. A driving license valid in Sweden (required for accessing the field sites) Merits: Merits
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, or are highly motivated to develop, the ability to analyze political decision‑making processes—from how policies are formulated and implemented to how they are reassessed and transformed over time. The admitted
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) scientific studies Experience with relevant field data collection methods (e.g. chamber- or eddy covariance-based C flux measurements, biodiversity sampling methods) Computer programming skills (e.g. Matlab, R
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/ Project description Graphs are a fundamental way of representing structured information from sentences and computer programs to social networks and they are becoming increasingly important as generative AI