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networks in order to enhance fog net technology. The planned work is experimental and will be conducted in our lab facilities, also incorporating theoretical models of complex flow. Fieldwork is planned in
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Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the methodologies
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at Integreat - Norwegian Centre for Knowledge-driven Machine Learning is a Centre of Excellence, funded by the Research Council of Norway. Researchers at Integreat develop theories, methods, models, and
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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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, immunoprecipitation and confocal microscopy. Experience with cancer organoid models and/or bioinformatics is an advantage. We offer broad training possibilities in the required experimental methods within a stimulating
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) and economics (or related fields). Applicants must have experience in one or more of the topics: Model-predictive control Numerical optimization Econometrics Virtual power plants Power systems and/or
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analysis. This digital transformation has also paved the way for innovations like AI-assisted morphological analysis. This project will research a self-produced AI model for automatically classifying plasma
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generative AI models are replicated and perhaps exaggerated in the output of generative AI, and that this could lead to culturally specific narrative biases. Applicants are required to submit a detailed
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management expert and a research assistant. AI STORIES explores the hypothesis that deep narrative structures in the datasets used to train generative AI models are replicated and perhaps exaggerated in
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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