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Field
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science, or a related field. Strong background in quantitative methods and statistical analysis. Experience with computational tools for large-scale data analysis (e.g., Python, R, SQL). Familiarity with
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., Zimmermann V., Dardalhon V., Campillo Poveda M., Turtoi E., Thirard S., Forichon L., Giordano A., Ciancia C., Homayed Z., Pannequin J., Britton C., Devaney E., McNeilly T. N., Berrard S., Turtoi A., Maizels R
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, cognitive science or a related discipline, and a keen interest in developmental research. Prior experience with experimental design, data analysis (preferably in R) or developmental populations is highly
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trustworthy ICT systems and services. We play an instrumental role in Europe by fueling innovation through research partnerships with industry, boosting R&D investments leading to economic growth, and
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processes with relevance for irrigation and drainage Have knowledge in programming in R and Python for statistical analyses and modelling. This includes experience in applying these skills in projects Have
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activities – Have postdoctoral experience at Spanish and/or foreign universities or R&D centers other than IBEC. Stays or mobility while providing services to IBEC. – any other contribution that makes it
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a completed or in-progress Ph.D Good knowledge of quantitative research methods and data in the social sciences, and statistical programs for handling them (e.g., Python, R, and Stata) Experience
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and scored: A. Knowledge of Matlab, Python, R and/or C++ (advanced level). These will be assessed based on accredited training hours (0.5 points for every 10 hours of training, up to 5 points for each
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Matlab, Python, R and/or C++ (advanced level). These will be assessed based on accredited training hours (0.5 points for every 10 hours of training, up to 5 points for each course) and accredited project
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: - for students enrolled in a degree-conferring course to carry out R&D activities with the aim to consolidate their scientific training through the development of research work leading to the attainment