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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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(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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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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Solutions”, an interdisciplinary project integrating 2D and 3D seismic and well data with state-of-art numerical forward modelling to understand the role of salt basin geometry and intra-salt heterogeneity
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how people estimate quantities. The project combines theoretical modeling with behavioral experiments to advance our understanding of the cognitive processes that connect our sense of magnitude with
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, who is leading studies on metabolism and signaling, closely collaborating with Prof. Ines Heiland, who is an expert in metabolic modeling. Active participation in and contribution to joint project
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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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Council of Norway. Researchers at Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational
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theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the methodologies of statistics, logic