875 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions in Sweden
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Swedish. Closing date for applications: January 12, 2026 Reference number: 6020-2025 URL to this page https://web103.reachmee.com/ext/I003/583/main?site=6&validator=e4575239eb8c0828707e2b716f86c5f8&lang=UK
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background and interest in soil microbial ecology, ecosystem ecology and biogeochemistry. You will be part of the Microbial Biogeochemistry in Lund (MBLU) research environment (https://portal.research.lu.se/en
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collaboration with wider society. 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
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, holiday leave, and occupational health services. Read more about the benefits of being an employee at Umeå University here: https://www.umu.se/en/work-with-us/benefits/ . Application You apply via our e
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to submit your claim. #LI-DNI URL to this page https://web103.reachmee.com/ext/I018/1151/main?site=8&validator=2efd9e54ee423d53334ac7960e3b4e03〈UK&rmpage=job&rmjob=3370&rmlang=UK
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Doctoral student in Materials Chemistry of Doped Organic Semiconductors in EU Training Network FADOS
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=14188&rmlang=UK
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will also use focussed ion beam milling scanning electron microscopy (FIB-SEM) to prepare infected cells for in situ cryo-ET. The resulting tomographic data will be analysed by machine-learning assisted
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of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as flow matching. Therefore, the doctoral
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focus on innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop
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, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with an emphasis on maintaining physical consistency