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-scale genetic data. The project is embedded in the Statistical Genetics Psychiatry Group within the department of Psychiatry at Amsterdam UMC and is funded by the European Research Council (ERC). As a PhD
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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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, programming languages, scientific computing, and statistical physics communities. Depending on your background and interests, your project could involve: developing new differential and probabilistic
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various data sources, including nationwide registries from Statistics Netherlands and large-scale longitudinal cohort studies, to investigate CHD, MI- and ischemia-related outcomes, intermediates, and risk
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Statistics Netherlands and large-scale longitudinal cohort studies, to investigate IS and ischemia-related outcomes, intermediates, and risk factors. You will enrich these data sources with relevant
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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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interested in how floods are generated and which processes can lead to floods? Do you want to understand how extreme events manifest and how they differ in space and time? Do you want combine statistical and
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, mathematical logic or statistical learning theory. For PhD position 2, we appreciate prior experience in implementing deep learning models for graphs and networks. Our offer As a PhD candidate at UT, you will be
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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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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