73 evolution "https:" "https:" "https:" "https:" "https:" "https:" positions at Chalmers University of Technology in Sweden
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to the development of a bio-based, sustainable society. In particular, we focus on microbial processes and enzymes acting on renewable biomass. The division has an extensive instrument platform for analytics
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bacteriology, we work with applications of various nanomaterials in biomedicine and biotechnology, metabolic engineering of microbial cell factories and experimental evolution. Our work is focused on societal
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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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Infrastructure? No Offer Description We are looking for a Doctoral student to become part of our team at the Division of Product Development at the Department of Industrial and Materials Science. Join our
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of Microtechnology and Nanoscience advances the frontiers in quantum technology, nanoscience, photonics and future electronic systems - for technical and societal development. Our cross-disciplinary approach gives
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This project focuses on the development of quantum–classical modeling strategies for multiphase flow systems. The PhD topic is on exploring how emerging quantum computing methods can be integrated with classical
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heat or pressure, to transform reactants into products. Electroorganic synthesis offers tremendous potential to realize this vision. However, its large-scale implementation requires the development
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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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, innovation, and education, we contribute to the development of future industrial value chains. In the division of Computational Mechanics and Materials Engineering , we foster an inclusive, friendly, 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