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methods to draw causal inferences from non-experimental data. The main data source is administrative register data linked with large-scale survey data and genetic data. The successful candidate should have
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an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural
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an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural
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data, computationally intensive inference for complex models, causal inference and survival models, measurement uncertainty, and research for clinical trials and observational studies - and numerous
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, with interests spanning a broad range of areas - including statistical machine learning, high-dimensional data and big data, computationally intensive inference for complex models, causal inference and
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appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods
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academic achievements in previous studies. Demonstrated knowledge of statistics and causal inference methods / econometrics, including good results in advanced courses. Experience with programming and
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. FATES simulates and predicts growth, death, and regeneration of plants and subsequent tree size distributions by tracking natural and anthropogenic disturbance and recovery. It does this by allowing
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track record experience in collection-based research (both physical and/or digital) teamwork and networking skills Personal skills: We are looking for a highly motivated, creative, and structured
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MIMICS+ module for soil carbon decomposition. FATES simulates and predicts growth, death, and regeneration of plants and subsequent tree size distributions by tracking natural and anthropogenic disturbance