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IKTPLUSS-IKT og digital innovasjon

PhD in Computational Geoscience

Awarded: NOK 2.3 mill.

The research project concerns machine learning techniques applied to remote sensing imagery and computational sediment transport simulations of river deltas. This is important e.g. for CO2-storage, hydrocarbon reservoirs and groundwater flow. Satellite data from the USGS Landsat program and ESA's Copernicus program is used. The Delft3D software, developed at the Deltares research institute in the Netherlands, is used for the sediment transport simulations. The results show that we can use graph theory and Generative Adversarial Networks, a modern machine learning method, to generate prior geological models. These methods are much faster than classical geostatistics.

Tre innlegg på internasjonale konferanser Fem innlegg på møter med industripartnere på Stanford University Forventete resultater i 2019 er to artikler og en avhandling.

The proposed project is a four-year Ph.D. program at the Department of Energy Resources Engineering and the Institute for Computational and Mathematical Engineering at Stanford University, United States. The main objective is to obtain significant new res ults from independent research within the field of computational geoscience. A milestone will be to pass the qualification examination and the Ph.D. project proposal defense after the first academic year. I will also remain affiliated with the Norwegian C omputing Center, where I am currently employed as a research scientist, during this period. The exact topic of the research project is not known yet. Since I have received a Fulbright Scholarship for the first academic year (2014-15), I am obliged to ret urn to work in Norway for a minimum of two years after graduation in 2018. It would therefore be advantageous to work on a project which is relevant for contract research at the Norwegian Computing Center or that fits with the research opportunities at th e University of Oslo or at NTNU.

Funding scheme:

IKTPLUSS-IKT og digital innovasjon