56 evolution "https:" "https:" "https:" "U.S" Postdoctoral positions at Chalmers University of Technology
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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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European Universities in several EU aviation projects About the research project EXAELIA The project is related to the development of future aircraft concepts and propulsion technology that has
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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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Transfer Operators [4 ,5 ]. The Postdoc will lead both the conceptual development in close collaboration with the project’s Principal Investigator, and practical implementation of this research with
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, in close collaboration with wider society. Chalmers was founded in 1829 and has the same motto today as it did then: Avancez – forward. Where to apply Website https://academicpositions.com/ad/chalmers
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to the application deadline What you will do In this poisition, you will be central to the development of the project, and also responsible for the implementation, validation and data analysis of the numerical tools
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technology, nanoscience, photonics and future electronic systems - for technical and societal development. Our cross-disciplinary approach gives interesting collaborations in academy, industry and society, 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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collaboration meet. The research topics in the department span fundamental and applied research to contribute to the development of a sustainable society. We are Sweden's largest mathematical department, with
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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