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(RAG) models – are shaping professional expertise and practice across diverse Danish public sector domains, especially among frontline workers, including caseworkers, service providers and welfare
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on climate impact research and process-based modelling of biogeochemistry, agronomy, biology and geography from Aarhus University and University of Copenhagen, as well as international partners. Field
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on climate impact research and process-based modelling of biogeochemistry, agronomy, biology and geography from Aarhus University and University of Copenhagen, as well as international partners. Field
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involve the following tasks: Supervision of BSc/MSc and PhD students related to the project. Performing catalytic tests on upgrading pyrolysis oil and pyrolysis oil model compounds using an advanced
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-scale research facilities, such as synchrotrons, for EXAFS and high-pressure XPS measurements. We also work in close partnership with the CatTheory group, using DFT-calculations and microkinetic modelling
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Postdoc in assessing carbon sequestration potential of different wetlands as nature-based solutio...
comprehensive quantitative evidence and understanding of their capacity and cost-effectiveness for carbon sequestration under varying conditions. You will be part of a large international research project focused
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processing conditions, and minimizing environmental exposure during final application. The assessments are crucial for validating safer alternatives and sustainable process designs. Qualifications
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to modify their function in cellular models in tissue culture together with external collaborators. In addition: The lab has a focus on innovation in science and the project has an application oriented focus
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proteins as food ingredients in food models Conduct project reporting and publishing results in international scientific journals Participating in teaching and supervision of students at all levels As a
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, protocols, and data standards across collaborating institutions and scales. This collaboration will support the generation of coherent, high-quality datasets and enable the development of predictive models