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and mortality registry, community-embedded settings for participatory research, and cutting-edge methodological expertise in causal inference and artificial intelligence methods for epidemiology and
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this knowledge gap and establish improved GHG models accounting for soil invertebrates. To achieve this, we create a rich AI-training dataset for multi-modal inferences, combining computer-vision, environmental
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annotation, image recognition, data extraction); Development and maintenance of statistical software tools for causal inference and open science applications. Your qualifications profile Enrolment in a
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for multimodal inferences, combining computer-vision, environmental parameter measures and DNA data. Your role will be central in data acquisition and foremost machine-learning models creation. You will
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. To achieve this, we create a rich AI-training dataset for multi-modal inferences, combining computer-vision, environmental parameter measures and DNA data. Your Tasks Participation in fieldwork in Germany
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-analysis techniques, causal inference and quasi-experimental methods is a strong asset; Familiarity with reference management tools (e.g., Zotero, Mendeley) and systematic review tools (e.g., EPPI, Covidence