53 evolution "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" Postdoctoral research jobs 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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, renewable energy, to chemical engineering processing, material recycling, nuclear chemistry, theory and modelling. About the research project The project focuses on the development and synthesis of new π
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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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. Strong written and verbal communication skills in English. Demonstrated experience with machine learning methods and research software development. Basic knowledge of wireless communication systems. A
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to the development of a sustainable society. We are Sweden's largest mathematical department, with around 200 dedicated staff, including 65 PhD students . We are a joint department between Chalmers University
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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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and Plasma Physics (AoP), we investigate the origins and evolution of the Universe – from star and planet formation to galaxy dynamics and the physics of fusion plasmas. Through international
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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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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