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on the development of novel enzyme-based conversion concepts for degradation of synthetic polymer materials. Field of Research The field of research is advanced industrial enzyme technology. A particular target of the
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Researcher in Computational Heterogeneous Catalysis Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", fill out
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of Copenhagen (UCPH) and European Spallation Source. The European Spallation Source (ESS) in Lund (SE) is a future large-scale research facility for neutron-based investigation of materials in a broad
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photon pairs and entangled photon states in waveguide devices based on silicon carbide material. The overall aim is to demonstrate devices for application in quantum communication. The PhD student will
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containing all materials to be given consideration. To apply, please open the link "Apply now", fill out the online application form, and attach all your materials in English in one PDF file. The file must
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assessment database: https://ufm.dk/en/education/recognition-and-transparency/find-assessments/assessment-database . Please note that we might ask you to obtain an assessment of your education performed by
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of polymers and other materials such as metals and dielectrics, but also characterization of those. You will be leading the laboratory work. These activities will be in close collaboration with our partners
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of natural hydrogen generation and resources in Greenland Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", fill out
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about DTU Aqua at https://www.aqua.dtu.dk/ ; and the section for Fish and Shellfish diseases here: https://www.aqua.dtu.dk/english/research/fish-and-shellfish-diseases If you are applying from abroad, you
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doctoral candidates with skills in interdisciplinary research, spanning from nanotechnology and materials science to molecular microbiology, biochemistry, and infection medicine to AI-based data analysis