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. Experimental work could include the design, construction, and testing of prototype storage systems, while simulation efforts may focus on thermal modelling, system optimization, and safety analysis
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well as of the cyclone family itself. The candidate will investigate the mechanisms by which moisture is drawn into and processed within cyclone families using reanalyses, idealised and realistic model simulations
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of the art in mathematical and numerical modelling of CO2 storage? This might be the right position for you! About the project/work tasks: About the project TIME4CO2 TIME4CO2 aims at advancing simulation
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. Integreat develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. We do this by combining
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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 collaboration with a non-profit
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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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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 academic environment in
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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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updates, and interoperability at scale. In close collaboration with project partners, the PhD candidate will focus on relevant data modeling and processing approaches for data gap filling, redundancy
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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