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The net uptake of carbon to terrestrial systems (LULUCF) in Norway is estimated to be 20-25 MtCO2e/yr or about 50% of the anthropogenic greenhouse gas emissions. However, the positive trend in the estimated
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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 for knowledge graphs (KGs), which
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(IRT) models in small samples. The ideal candidate has prior knowledge of IRT models, a basic understanding of common estimation methods, and strong programming skills in R, Python, or another relevant
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knowledge of IRT models, a basic understanding of common estimation methods, and strong programming skills in R, Python, or another relevant computing language. Experience with machine learning methods is a
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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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Fellowship at the University of Oslo. Project description The net uptake of carbon to terrestrial systems (LULUCF) in Norway is estimated to be 20-25 MtCO2e/yr or about 50% of the anthropogenic greenhouse gas
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Research Fellowship period at the University of Oslo. Jobb description The person hired in the position will work on theoretical control and optimization methods for a grid island powered by renewable energy