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developed models will be applied to estimate design flood events for different return periods and flood types. With metrics based on flood statistical aspects, the type-specific models will be compared
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experience in pytorch and/or tensorflow) knowledge of statistical methods and programming (R and Python) prior experience in machine learning and ideally also on applications in the health domain and/or
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relationship from the perspective of staff and youth (Study 4). You will use innovative observational methods and state-of-the-art statistical analyses to investigate significant predictors of the process
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of statistics. Programming expertise in Python, or similar languages, with experience in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Excellent communication skills in English, both
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statistical models to predict treatment response; optimizing individualized rTMS targeting using neuronavigation and computational modeling; designing and conducting n-of-1 trials embedded in routine clinical
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, alongside advanced understanding of genetic resource conservation and forest ecology. Technical proficiency: Experience with GIS, databases, and genetic and statistical methodologies; familiarity with R is
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strongly preferred Strong interest in wellbeing and beyond GDP Knowledge of statistical and spatial analysis methods and tools, or willingness to learn Proficiency in English, both spoken and written We
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, NVivo). Proficiency in Excel, SPSS, and other statistical tools. Full fluency in English; proficiency in Dutch is highly desirable. Ability to communicate complex findings to diverse audiences. Excellent
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quantitative research methods (e.g., statistical analysis, survey design). An interest in EU health data governance, including the EHDS regulation, and health information systems. Excellent written and spoken
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able to work with very large data and have the creativity and curiosity to work with network measures and statistics. You may also be an expert on grounded computational social science: combining large