-
computing (HPC). This position offers an exciting opportunity to work on cutting-edge research projects at the intersection of numerical linear algebra and advanced HPC. The candidate will join an
-
. The objective of the research is to use an integrated approach combining numerical and analytical techniques, simulations and analysis of available experimental data to study and provide efficient
-
the coupled numerical model that we have developed for surface and subsurface flows. We will use finite volumes methods combined with physics-informed neural networks (PINNs) which offer a flexible technique
-
provision of user-tailored climate services including products related to climate modeling. The successful candidate will have experience in two or more of the following areas: numerical climate modeling
-
numerous applications, including electric vehicles and the integration of renewable energy sources. In light of the circular economy and the principles of responsible consumption and production, utilizing
-
numerous applications, including electric vehicles and the integration of renewable energy sources. In light of the circular economy and the principles of responsible consumption and production, utilizing
-
even observing all the components is a challenging task. In most cases, only a sample of a network is observed. Therefore, network completion needs to be addressed. Matrix completion methods have proved
-
, network completion needs to be addressed. Matrix completion methods have proved to be efficient when reconstructing a non fully observed data. These methods can be applied to complete or predict links in a