88 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" positions at University of Lund in Sweden
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Professor Emma Ahlqvist and consists of research and technical staff. The group is located at CRC, Malmo. Here you can read more about our exciting research: https://www.ludc.lu.se/research/genetics-and
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administrative duties, up to a maximum of 20% of full-time. The working language is primarily English, but you are expected to acquire basic knowledge of Swedish during the employment period. More information
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and software Experience in developing technical documentation, safety procedures, and audit reports Experience with automation technology Experience with machine safety and/or process safety systems
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, econometrics, applied microeconomics, and macroeconomics. For more information, please visit: https://www.lusem.lu.se/organisation/department-economics/research Job Assignments The holders of these positions
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well as in the everyday activities of the project, including reporting to FORMAS. More information about the project: https://portal.research.lu.se/en/projects/power-and-polarisation-in-swedish-forestry-from
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: https://www.soclaw.lu.se/en/index.php/article/sociology-law-department-leads-eu55m-eu-funded-research-authoritarian-law-and-legality-central-asia https://ddrn.dk/18550/ Tasks and responsibilities As a
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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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using EEG and advanced machine-learning approaches. The project focuses on identifying neural signatures of recognition and memory retrieval at the single-trial level, with particular emphasis on time
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researchers from literacy, STEM, special didactics, and the science of learning to create an interdisciplinary environment applying evidence-based practice. By systematically developing and implementing
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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with