882 machine-learning "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" positions in Sweden
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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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accordance with Karolinska Institutet’s template (http://ki.se/qualificationsportfolio) . You may change or add to your application at any time up to and including the application deadline date. After
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Engineering and will become part of the national graduate school FOFOS – Research School for the Transformation of the Public Sector (https://www.mdu.se/forskning/forskarskolor/forskarskolan-fofos ). FOFOS is
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://www.academiceurope.com/ads/software-engineer-w-m-d-fur-batteriespeicher-analyse/ Do not overlook the inclusion of Academic Europe in your application. Where to apply Website https://www.academiceurope.com/ads
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information about the department/division: https://www.slu.se/institutioner/vaxtbiologi-skogsgenetik/ Read more about our benefits and what it is like to work at SLU: https://www.slu.se/om-slu/jobba-pa-slu
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Computational Mathematics) and some departmental duties, mainly teaching for basic level courses. Teaching can be performed in English, and there is support for learning the Swedish language if desired
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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
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-microbe interactions, mycology, and microbial ecology. The position is placed in the Forest Microbiology group. Read more about what it is like to work at SLU at: https://www.slu.se/en/about-slu/work-at-slu
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University of Gothenburg, Department of Education, Communication and Learning | Sweden | about 2 months ago
5 Feb 2026 Job Information Organisation/Company University of Gothenburg, Department of Education, Communication and Learning Research Field Educational sciences Researcher Profile First Stage
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to machine learning is well funded and continuously publishes in high impact journals. We foster a creative working environment, where you will find freedom to implement, develop, and publish research