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performance and future potential. The successful candidate will work under the main supervision of Prof. Holger Voos and will be required to perform the following tasks: Carry out the aforementioned research
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PhD position - Stress-testing future climate-resilient city and neighbourhood concepts (Test4Stress)
also supervised by Prof. Dr. Jana Sillmann, who is co-leading the Research Unit for Sustainability and Climate Risks (FNK). This Research Unit is devoted to inter- and transdisciplinary research and
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the position within a reasonable time after receiving the offer. The position’s field of research Graphite has been classified as a Critical Raw Material (CRM) for the EU/EEA economy due to its essential role in
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candidate will be supervised by Prof. Francesco Maresca, chair of the Mechanics of Materials unit of the Engineering and Technology institute Groningen (ENTEG). Organisation Founded in 1614, the University
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environment and to acquire valuable research experience. The project is funded by a bequest to the university and should include the archaeological record of the southeastern part of the province of Friesland
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vehicles use power electronic converters to transfer energy from the source to its final use. Modern power modules comprise arrays of devices made of wide-bandgap (WBG) semiconductor materials operating in
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thermochemical TES. Your main supervisor will be Prof Adriano Sciacovelli and you will join the Thermal Energy Section at DTU Construct. Your work will contribute to a paradigm shift in how complex TES systems
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-leading research in several fields. Notably, the groundbreaking discovery of the CRISPR-Cas9 gene-editing tool, which was awarded the Nobel Prize in Chemistry, was made here. At Umeå University, everything
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for PhD positions. As a member of our team, you will have the opportunity to collaborate on cutting-edge technologies like quantum simulators, Bose-Einstein Condensates, and quantum information processing
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calculations of well-characterized 2D materials, simulations of electron microscopy images, and machine learning methods to reconstruct the 3D atomic positions of materials from a 2D microscopy image. The