411 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" uni jobs 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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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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community of Digital Research Engineers. The offices also host computer infrastructure and machine learning/data science/research data management experts, who develop, build, and manage the local and national
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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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seamless connectivity across terrestrial and non-terrestrial segments. In this context, data-driven, artificial intelligence (AI) and machine learning (ML)-native approaches are essential for the design and