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inference, selection scans, and gene-environment and gene-phenotype association studies. • Plan and conduct fieldwork to collect plant material across Arctic locations, and manage sample processing
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implement a framework to infer anisotropic viscosity from both ice and mantle textures in a numerical flow model. This will open new avenues for understanding solid earth and cryosphere dynamics, and their
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genomic convergence. • Carry out population genomic analyses, including demographic inference, selection scans, and gene-environment and gene-phenotype association studies. • Plan and conduct
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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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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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evolving baselines and complex, data-intensive settings. The applicant must master theoretical work in the field of change and anomaly detection and estimation, as well as having a strong track record in