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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
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selection criteria Peer-reviewed publications in relevant fields. Experience with modelling and simulation, e.g. machine learning, parametric design, or finite element tools (Abaqus, Ansys, etc.). Relevant
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the extent to which you have relied on such models. Copy of diploma for both bachelor and master education and relevant certificates Copy of the applicant’s master’s thesis as a PDF file Name and contact
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interested in programming and developing the next generation models for inflow forecasting? Work with us, SINTEF and the Hydropower industry to develop State of the art models for better water management. The
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, or using climate modeling such as the Norwegian Earth System Model to constrain models and observations. Your immediate leader will be the Head of Department. About the project The PhD candidate will join
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seasonal emissions such as winter CH4 emissions, using AI tools to develop upscaling tools or upscale to circumpolar region, or using climate modeling such as the Norwegian Earth System Model to constrain
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of smart technologies to visualize yard operations in a digital form (such as virtual models and digital twins). Smart technologies can collect, analyze, and represent data from various sources
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for simulating flow and pressure response, as well as thermal storage. Also analyzing new and existing data and calibrating models. Duties of the position Complete the doctoral education until obtaining a
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management using (and adapting) petroleum/CO2 storage tools for simulating flow and pressure response, as well as thermal storage. Also analyzing new and existing data and calibrating models. Duties
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models to resolve blade loads and structural responses under both operational and extreme conditions, including scenarios with partial out-of-water exposure Uncertainty quantification to ensure robust and
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access. The goals of such access include supporting registry operations as well as health care research. Of particular interest in this context are differentially private algorithms for statistical model