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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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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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, 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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predictions. To mitigate these effects, advanced ML techniques such as Bayesian deep learning, probabilistic models, and uncertainty quantification methods can be applied to enhance model robustness
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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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demonstrated through previous engagement. Demonstrated knowledge of statistics and causal inference methods / econometrics. What are we looking for in you? Good collaboration skills as well as ability to work
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of a postdoctoral position is to build up an academic track record that qualifies you for a position as assistant or associate professor. The position also offers additional skills that are to your
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tasks independently and contributing effectively to a research team. A proven track record (given the career stage) of peer-reviewed publications in relevant scientific journals. Excellent written and