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learning, for offshore industrial produced water treatment processes. The developed methods/solutions should be tested and demonstrated on a globally leading pilot-plant sited at Aalborg University
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computational methods to predict novel, disordered materials with application, e.g., for solid electrolytes in next-generation batteries. The position is part of the Villum Young Investigator project titled
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the following areas: Air and surface sampling of fungi in buildings and on materials Basic statistical analysis knowledge (R or similar) and multivariate statistical methods Cross-disciplinary research
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collaboration with PI Associate Professor Mette Simonsen Abildgaard. Qualification Requirements • Applicants should hold a PhD in anthropology, STS, arctic studies, media studies or related fields
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Postdoc – Performance requirements for biobased construction materials used in the building envelope
materials, including their hygrothermal properties, durability, and performance limits in building applications. Competence in laboratory testing and development of test methods is essential, as is experience
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University is known worldwide for its high academic quality and societal impact. The Department of Electronic Systems employs more than 200 people, of which about 90 are PhD students, and about 40 % of all
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. We expect applicants to hold a PhD in a relevant field such as techno-anthropology, science and technology studies, human-computer interaction, human-robot interaction, digital health, anthropology
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to developing methods and tools that support a cost-effective, flexible and sustainable integration of cooling in future energy systems. Your work will be structured around two strongly interconnected research
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-experimental data from five countries. Methodologically, the project designs and implements survey experiments and other causal inference methods (e.g. difference-in-differences and synthetic control). Your
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has: A PhD in translational and laboratory medicine within the field of IBD. Proven expertise in omics data analysis, including proteomics and metabolomics data (R/RStudio, multivariate and regression