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to understand how biodiversity and ecosystem functioning are linked and changing across different habitats in response to climate change and/or human use of natural resources. The successful candidate will
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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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and statistics, with expertise spanning time series analysis, Bayesian inference, financial econometrics, and data analytics. As home to one of the strongest forecasting research groups worldwide, we
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the different crystals might have different orientations. As metals are typically used for load-bearing applications, it is imperative to understand the mechanical performance of such materials. Initially, metals
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shown that DLVM models can be extended with success to at least two different types of data (network and texts, text and images, …) but the extension to several data types is still difficult in the sound
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detection model to a more flexible unequal-variance model in a hierarchical Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling and simulation techniques
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Aim/outline Graphs or networks are effective tools to representing a variety of data in different domains. In the biological domain, chemical compounds can be represented as networks, with atoms as
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coronavirus), and the production of renewable energy in different countries are some examples. In almost all contexts, these episodes happen in several time series, but not necessarily at the same calendar