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involves development of deep learning based synthetic data generators that obtain both good utility and protection of privacy, through tailored model approximation, as well as new measures of privacy and
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, who are eager to contribute to cutting-edge methods for generating private and fair synthetic data with good utility. This project involves development of deep learning based synthetic data generators
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research projects and centers. (See for instance http://www.mn.uio.no/geo/english/about/organisation/geohyd and https://www.mn.uio.no/geo/english/research/groups/remotesensing ). We are a growing, lively
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investigating the terrestrial cryosphere and is involved in various high-level research projects and centers. (See for instance http://www.mn.uio.no/geo/english/about/organisation/geohyd and https
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for Physical Geography and Hydrology, which has long-term experience in investigating the terrestrial cryosphere and is involved in various high-level research projects and centers. (See for instance http
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research projects and centers. (See for instance http://www.mn.uio.no/geo/english/about/organisation/geohyd and https://www.mn.uio.no/geo/english/research/groups/remotesensing ). We are a growing, lively
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), it must be equiva-lent to a master in the Norwegian educational system Documented proficiency in scientific programming (e.g., Python) Documented proficiency in deep learning frameworks (e.g., PyTorch
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algorithms, representation and learning of data and dynamical systems, geometric deep learning, topological and algebraic data analysis, optimization on manifolds, and operator- and PDE-based approaches
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for roughly 3% of global CO₂ emissions, and with international pressure to decarbonize hard-to-abate sectors, nuclear propulsion is emerging as a serious candidate for deep-sea and high-utilization vessels
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project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD fellow will be part of a growing