73 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" positions at University of Lund in Sweden
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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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, 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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opportunity to take on a diverse and high-impact role within a growing and dynamic research environment. For more information: https://www.nano.lu.se/facilities/lund-nano-lab We offer In this position, you will
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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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you are expected to acquire basic knowledge of Swedish during the employment period. More information about the doctoral programme is available at: https://www.soclaw.lu.se/en/research/doctoral-studies
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conferences, as well as opportunities to participate in pioneering interdisciplinary projects. More information about the doctoral programme is available on the department’s website: (https://www.iko.lu.se/en
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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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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
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that are commonly used today. Using the improved noise models, machine learning methods will be used to enhance the segmentation of EEG data into auditory signal and background activity allowing for refined control
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particular, the candidate will work on the development of novel deep-learning reconstruction algorithms to retrieve 3D and 4D (3D+time) imaging acquired by advanced X-ray imaging techniques. Such developments