907 machine-learning "https:" "https:" "https:" "https:" "U.S" "U.S" positions in Sweden
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cases, this can be slightly prolonged). The position will be placed at Timur Shegai's research group at Chalmers. You can find more information about the group at: www.shegai-lab.com or https
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international academic institutions and 14 industry partners (https://euraxess.ec.europa.eu/jobs/401249 ). We work together in the field of fluid-structure interaction in technical systems and industrial
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. Chalmers was founded in 1829 and has the same motto today as it did then: Avancez – forward. URL to this page https://www.chalmers.se/en/about-chalmers/work-with-us/vacancies/?rmpage=job&rmjob=14708&rmlang
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will be found on our career site: https://www.oru.se/english/career/available-positions/applicants-and-external-experts/ The application deadline is1st of April, 2026. We look forward to receiving your
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ways to transform towards them. Finally, we will synthesize our learning across cases to enhance causal multispecies understanding of biodiversity. The postdoctor will work with the Swedish team but is
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Python) and data analysis or machine learning applied to materials science Ability to work in interdisciplinary project or industrial experience About the employment The employment is a temporary position
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competitive advantage (https://liu.se/en/research/cbmi ). You will work under the supervision of Professors Christian Kowalkowski and Daniel Kindström. Research at IEI spans a broad range of areas, from
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and free-energy calculations in explicit solvent. The postdoctoral researcher will employ machine-learning-accelerated methods throughout the workflow, contribute to the development of new computational
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to the forefront of quantum technology, and to build a Swedish quantum computer. Building a quantum computer requires a multi-disciplinary effort involving experimental and theoretical physicists, electrical and
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, or related areas, fields or environments. We expect experience and competences in one or more fields of research on late working life; labour markets; public, branch and employer policies; lifelong learning