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models, aiming to reduce CO₂ emissions and improve resource efficiency through enhanced data-driven lifecycle management. A DPP can be viewed as a structured, machine-readable knowledge artifact
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and concepts with existing seismic models. The project will involve collaboration with industry partners and other scientific teams. The candidate will work alongside geoscientists in the BASINS section
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high-throughput sequencing data analysis (e.g., CAGE, ATAC-seq, ChIP-seq, or Hi-C) Expertise in statistical modeling for biological data Knowledge of enhancer-promoter interactions and 3D genome
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for plausible narratives of regional climate change, novel algorithms for rare event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components
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their speciation under reaction conditions, as supported through computational modelling. The position will be part of the Section for Catalysis and Organic Chemistry . The Section for Catalysis and Organic
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. Researchers at 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
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placed 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