83 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" positions at University of Lund
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the Horizon Europe framework. To read more about the SOCIAL project please see: https://www.soclaw.lu.se/en/index.php/article/sociology-law-department-leads-eu55m-eu-funded-research-authoritarian-law-and
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projects. More information about the doctoral programme is available on the department’s website: (https://www.iko.lu.se/en/research/doctoral-studies). Eligibility General eligibility for third-cycle
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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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, 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
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. Demonstrated experience in computational methods, particularly in deep learning and computer vision. Understanding or willingness to learn advanced statistical modeling is a plus Assessment criteria and other
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. Particular emphasis will be placed on research skills within the subject. Additional assessment criteria: Experience in machine learning Experience in image reconstruction, specifically in 3D and 4D
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of research? Find more reasons why Lund University and the HT Faculties is right for you here , and learn more about Working in Lund , Moving to Lund and Living in Lund . Qualifications The assessment will
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University and the HT Faculties is right for you here, and learn more about Working in Lund , Moving to Lund and Living in Lund . Qualifications The assessment will primarily be based on your research merits
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Description of the workplace The Division of Secure and Networked Systems at the Department of Electrical and Information Technology conducts broad research in cryptography, computer security
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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