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Job Description Many industrial processes generate wastewater with small oil droplets stabilized by surfactants, the same goes for accidental spills of fuels incl biofuels and various process oils
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Human Centred Computing Section invites applicants for a 2-year Post Doctoral Fellowship in Computer Supported Cooperative Work (CSCW) in the NeuroGen Computing research project focusing on creating
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The Section for Electrical Energy Technology at the Department of Electrical and Computer Engineering (ECE), Aarhus University, is in a phase of rapid growth in both education and research
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twinning for production and process optimization. A first-of-its-kind pilot factory is now based at Aarhus University (AU Viborg), and has been equipped with about 200 sensors and a production management
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will join a vibrant group of interdisciplinary scientists in eg bioinformatics, cell biology, physiology and genomics. Project focus The postdoctoral researcher will lead experimental and/or
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Postdoc position to support international research and capacity-building projects employing elect...
assignments The successful candidate will contribute to the acquisition, processing, inversion, and interpretation of large-scale EM datasets for hydrogeological mapping in Ethiopia. The work will involve both
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physiology models of organs. You will join a vibrant group of interdisciplinary scientists in eg bioinformatics, cell biology, gene editing and material science. Qualifications Applicants should hold (or be
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Applicants should hold (or be close to completing) a PhD in biology, bioinformatics, genomics, or molecular physiology. We particularly welcome candidates with: Expertise in experimental genomics, cell biology
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, mechanical and durability testing, and integration with advanced machine learning models. The postdoc will collaborate closely with CEBE’s parallel work packages. Experimental and analytical data generated in
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across molecular biology, ecology, bioinformatics, and environmental science. The taxonomic scope is broad and inclusive: we aim to collect comprehensive data across multiple taxonomic groups to support a