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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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technological progress in our increasingly digital, data-driven world. Researchers in Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By
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Fellowship period at the University of Oslo. Job description The person hired in the position will work on theoretical control and optimization methods for a grid island powered by renewable energy, supporting
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UiO/ Anders Lien More about the position The position is tied to CREATE’s strand 3. Strand 3 aims to develop novel methods and statistical software that are tailor-made to the research questions and
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-of-the-art research methods for drawing causal inferences from non-experimental data. The successful candidate should have prior knowledge of quasi-experimental methods and, preferably, large data sources
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the genomic basis of convergent adaptation across independently evolved Arctic plant lineages, using a combination of population genomic analyses, common garden experiments, and comparative methods. More
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/Artificial Intelligence methods for automatic interpretation/classification of the RIMFAX radar images. Retrieval of geophysical parameters, such as dielectric properties, from RIMFAX data. Dissemination
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that SUMOylation of chromatin-bound proteins ensures the precision of gene expression and transcription factor dynamics during adipogenesis. However, the molecular mechanisms by which SUMOylation orchestrates
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flow law, compare it to similar research on olivine, and help implementing this model into the geodynamic software ASPECT. The aim is to develop a single method for anisotropic flow modelling for both
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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