79 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" positions at University of Lund
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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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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 reflective empirical analysis with strengths in intersectional perspectives on gender and sexuality and feminist methodologies. Read more about about our research themes here: https
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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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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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, 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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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