145 evolution "https:" "https:" "https:" "https:" "https:" "Washington University in St" positions at Chalmers University of Technology
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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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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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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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Akelius Math Learning Lab, see: https://www.chalmers.se/institutioner/mv/akelius-math-learning-lab/ Who we are looking for The following requirements are mandatory: Doctoral degree in mathematics
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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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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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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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- for technical and societal development. Our cross-disciplinary approach gives interesting collaborations in academy, industry and society, and is a driving force for innovations, results and breakthroughs. We
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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
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, etc.) development of predictive models and digital decision-support tools for nutrition and health method development in causal inference, integration of heterogeneous data sources, uncertainty