47 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" uni jobs at University of Lund in Sweden
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development, testing and application of the LPJ-GUESS biosphere model for modelling tropical wetlands and estimating tropical methane emissions. The work is part of the EU-funded project IM4CA (https://im4ca.eu
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Doctoral student in development of nanowire devices for photonic neuromorphic computing (PA2026/472)
on lu.se. https://www.lunduniversity.lu.se/about-university/work-lund-university About the employment The employment is afixed-term employment at full time, starting as soon as possible. Third cycle studies
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on lth.se. https://www.lth.se/english/study-at-lth/phd-studies/ Subject and project description ELLIIT (https://elliit.se) is a strategic research environment funded by the Swedish government in 2010, as part
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bachelor's and master's programs for both Swedish and international students. Read more at https://www.biology.lu.se/ . Find more reasons why Lund University and the Faculty of Science are right for you here
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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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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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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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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