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- University of Oslo
- NTNU Norwegian University of Science and Technology
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- NTNU - Norwegian University of Science and Technology
- Norwegian University of Life Sciences (NMBU)
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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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in one or more of the following research areas is desirable: geometric numerical integration, structure preserving deep learning, stochastic differential equations, generative AI, numerical
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of cryptographic implementations and hardware-security countermeasures. Experience with hardware reverse engineering, debugging interfaces, or firmware analysis. Experience with machine learning, deep learning
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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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, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their
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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
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for the position. Preferred selection criteria Solid theoretical background in robot perception and navigation. Deep foundation in modern machine learning. Solid programming skills in C++ and Python. Experience with
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is part of the ERC-funded 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