146 evolution "https:" "https:" "https:" "https:" "https:" "https:" "The University of Edinburgh" positions at Chalmers University of Technology in Sweden
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activities. Chalmers' researchers deal with basic research on the unique chemical and physical phenomena that prevail at very small dimensions, and promote the development of technologies that utilise
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and automated floor-plan recognition, to fill data gaps and harmonise information from disparate sources. Learn more and watch our project video here: https://sb.chalmers.se/digital-material-inventories
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future electronic systems - for technical and societal development. Our cross-disciplinary approach gives interesting collaborations in academy, industry and society, and is a driving force for innovations
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degree, obtained within the last three years prior to the application deadline Experience of teaching at undergraduate or master’s level, and an interest in further development within teaching and
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durability of engine materials. The project is expected to generate new knowledge and expertise that will contribute to the development of sustainable vehicle propulsion systems. An additional important
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future material development to more effectively treat arthritic diseases. As a project assistant with us, you will work with cell culture as well as model design and development in the context of arthritis
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that prevail at very small dimensions, and promote the development of technologies that utilise these phenomena. About the research project The topic of the PhD project will be about tunable self-assembled
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. Chalmers was founded in 1829 and has the same motto today as it did then: Avancez – forward. URL to this page https://www.chalmers.se/en/about-chalmers/work-with-us/vacancies/?rmpage=job&rmjob=14445&rmlang
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system developer for our national DDLS data services, with a profile towards data engineering, systems development, and data management. In this role, you are expected to develop software, systems, and
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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification