Franz Luef, Department of Mathematical Sciences, NTNU Henry McNulty, Department of Mathematical Sciences, NTNU, and Cognite AS Industry is awash with time series data, from
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Franz Luef, Department of Mathematical Sciences, NTNU Henry McNulty, Department of Mathematical Sciences, NTNU, and Cognite AS Industry is awash with time series data, from
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Kurusch Ebrahimi-Fard, Department of Mathematical Sciences, NTNU Fabian Harang, Department of Economics, Norwegian Business school, Oslo Fride Straum, Department of Mathematical Sciences, NTNU Propelled by
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Markus Grasmair, Department of Mathematical Sciences, NTNU Cervical cancer is one of the most common types of cancers in women worldwide and accounts for over
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REMODEL is a Staff Exchange programme funded by the Marie Curie Actions of Horizon Europe. In his article you find information about this funding scheme and a description of the min research topics of this programme investigating the mathematics of deep learning.
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This blog post on secure internet voting describes cryptology research at NTNU which has had impact on electronic elections in the Church of Norway.
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The vast oceans will always be under-sampled, but with the availability of satellite data and physics-based numerical ocean models one can gain nuanced insight about a variety of ocean variables.
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Geophisical data inversion is crucial for understanding properties of the subsurface.
The vision in this projects has been to
– provide creative mathematically based solutions to recognized challenges in reservoir evaluation,
– develop methodologies for analysis of spatial and spatio-temporal phenomena.
Background One of the main ways mathematical advances have impact in science, engineering, society, and industry is via their implementation in software. In order to
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The first time we organised MaGIC it was in 1999 in Ustahoset, Norway. The group of participants was not very big and we all lived
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by Andre’ Massing The burden of mesh generation The generation of meshes, as a fundamental prerequisite for the majority of computational methods for partial differential
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