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between ice and mantle dynamics. In DYNAMICE, we will implement a framework to infer anisotropic viscosity from both ice and mantle textures in a numerical flow model. This will open new avenues
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-of-the-art research methods for drawing causal inferences from non-experimental data. The successful candidate should have prior knowledge of quasi-experimental methods and, preferably, large data sources
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, school-level aggregated data, and genetic data. The successful candidate is expected to use state-of-the-art research methods for drawing causal inferences from non-experimental data. The successful
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of economics of innovation and the economics of ICTs / AI. Experience with one or more of the following empirical research methods will be considered an advantage: applied microeconometrics and causal inference
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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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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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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