933 evolution "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "St" "St" positions in Sweden
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organisms. Our main research areas include interactions between plants and microorganisms, and other forms of stress such as plant growth and development, biotechnology and metabolic manipulation, regulation
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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on strategies for sustainable development. The Institute facilitates a great number of external research and development projects as well as two international master programs. More information is available
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of the regional case studies and scale these into Pan-African scenarios. Main responsibilities The Postdoctoral Researcher will play a central role in the project by leading the development and deployment
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computing, networked systems, and beyond. The work will range from theoretical and algorithmic development of distributed protocols and coordination mechanisms, through the design and implementation
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languages (Johannes Borgström, Eva Darulova, Lars-Henrik Erikson, Sofia Ouhbi, Tjark Weber, Tobias Wrigstad). The department is known for both strong theoretical work as well as tool development in
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at https://nbis.se. Duties We are seeking a candidate who wants to help enable life science research in Sweden that goes beyond what is achievable by individual researchers, a single university, or a single
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technology, nanoscience, photonics, and future electronic systems — for technical and societal development. Our cross-disciplinary approach fosters interesting collaborations in academy, industry, and society
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solutions across the natural sciences. Your workplace You will be employed at the Department of Mathematics in the Division of Applied Mathematics, https://liu.se/en/organisation/liu/mai/tima . The research
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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