380 machine-learning-"https:" "https:" "https:" "https:" "https:" "UCL" uni jobs in Sweden
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. Requirements for the position is the ability to teach and conduct research in English, and it is desirable that within two years you can also teach and conduct research in Swedish. We are looking for you who
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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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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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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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on innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The specific focus is on development and
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data Expertisein advanced statistical analysis and machine learning methods Excellent English communication skills (spoken and written) We seek a creative, independent, and collaborative co-worker with
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thus MLOps (Machine Learning Operations), datacentric AI, and legal and ethical aspects of AI. The empirical research catalyzes industry-academia collaboration and cross-dsicplinary initiatives, in which