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robust methods for training models, exploring/developing approaches for multimodal data, utilize context beyond pixel level, and efficiently use self-supervision for large unlabeled data sets. You will
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archival data such as AKARI, DIRBE, FIRAS and Planck, ongoing projects like COMAP, PASIPHAE, Simons Observatory, SPHEREx and SPIDER, as well as future experiments like LiteBIRD and FOSSIL. We have a large
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how to benefit from recent research in foundational neural models that learn from large unlabeled image datasets, also incorporating context from additional data such as wireline logs or well reports
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methods for training models, exploring/developing approaches for multimodal data, utilize context beyond pixel level, and efficiently use self-supervision for large unlabeled data sets. You will transfer
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University of Oslo, Department of Mathematics, Section for Statistics and Data Science, invites applications for a PhD fellowship within the research project “Statistical Methods for Online Detection
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from Earth System Models is a requirement, as is experience working with large observational data sets. Any experience with NorESM would be highly advantageous. A strong background within the area of
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experience with running and analysing output from Earth System Models is a requirement, as is experience working with large observational data sets. Any experience with NorESM would be highly advantageous. A
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distributed data processing approaches to address issues of incomplete or redundant multimodal data, dynamic updates, and scalable semantic interoperability in large-scale DPP systems. Particular emphasis will
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to do joint end-to-end analysis of a wide range of cosmological datasets, including archival data such as AKARI, DIRBE, FIRAS and Planck, ongoing projects like COMAP, PASIPHAE, Simons Observatory, SPHEREx