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- NTNU - Norwegian University of Science and Technology
- University of Oslo
- NTNU Norwegian University of Science and Technology
- UiT The Arctic University of Norway
- University of Bergen
- Norwegian Meteorological Institute
- Norwegian University of Life Sciences (NMBU)
- The Norwegian School of Sport Sciences
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Field
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efforts to contribute to safer marine operations, we actively explore possibilities to utilize both numerical and machine learning methods to enhance the accuracy and resolution of metocean forecasts. About
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analysis and solid mechanics Experience with 3D computer-aided design (CAD) Experience in structural design, design format, and standards (e.g. Eurocodes) Personal characteristics To complete a doctoral
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foundation for theory-guided catalyst design e. g. by machine learning approaches. Duties of the position Complete the doctoral education until obtaining a doctorate Carry out research of good quality within
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models of fish species that simulate realistic deformation, motion, and interaction behavior. Explore how simulation outputs can be used to generate synthetic datasets for machine learning and AI
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, Amsterdam and Freiburg, will analyse the impact of blockades on households, states, corporations and the international order; on the development of political and military strategy; on how the wars were
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the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033. The project PI and team are also in close collaboration
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Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine learning in general. OCBE has numerous collaborations with
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Biological Dark Matter (BDM). A main idea of this project is to make use of the pattern matching abilities of the Tsetlin Machine in machine learning to be able to recognize signals in the BDM in
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interdisciplinary center with joint efforts in theory, computer simulations and experiments, both in fundamental and in more applied directions. The center works to advance the understanding of porous media by
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relevant experience in the development and deployment of machine/deep learning models as well as the use of remote sensing data You must have relevant experience in the development of hydrodynamic and water