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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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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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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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annealing). Interest and ability to work in interdisciplinary research. High proficiency in written and spoken English. Solid track record of peer-reviewed publications. Suitability for the position
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to the qualification requirements. Applications lacking this information will not be considered. Applicants must hold a PhD in life sciences, natural sciences or medicine, with a proven track record of cardiac research
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-processing (e.g., ParaView, Insight, NetCDF visualization) of simulation data. An emerging track record demonstrating publications in peer-reviewed, relevant journals. Excellent communication and English
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will be attributed to individual suitability and qualifications. Experience with JSON schemas, DevOps (deployment using Docker on Kubernetes), and use of databases for tracking requests and progress
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. A strong track record in peer-reviewed publications. Interest in writing and contributing to grant applications. We can offer A great opportunity to work in a diverse EU project with world-renowned