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about the departments is available at: Project description This PhD project will explore how Artificial Intelligence, in particular AI-based simulations, data-driven modelling, and generative systems
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demands. To break this bottleneck and cut simulation time by orders of magnitude, you will design and implement surrogate models that learn the behavior of full‑physics codes using modern machine‑learning
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methodology such as Thermodynamic modelling of multi-component planetary degassing/ingassing, Molecular Dynamic simulations of silicate melts, Petrology of melting of exoplanetary mantles, and the partitioning
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musculoskeletal geometries. Info . Novel reinforcement learning (RL) strategies to teach NMS models to execute a broad repertoire of movemets in presence of external disturbances. Info . Simulation-to-real-world
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future scenario simulation of VBD Including machine learning, statistical, and process-based models Present findings at scientific conferences and publish in peer-reviewed journals Contribute
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of an external magnetic field into the phase-field model and to simulate the microstructural evolution for different cooling rates. - Phase-field modeling of the phase transition in the Fe-Ni alloy. The selected
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. Previous experience in computational modelling, air quality or psychology is desirable. The Research Project The overall aim of this PhD project is to understand how indoor air quality (IAQ) affects
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this position will be responsible for conducting research in the area of verification, testing and exploration of artificial intelligence models, active participation in lab meetings, scientific seminars and
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Division of NORCE Norwegian Research Centre there is a 3-year full time PhD research fellow position available within the field of modeling and simulation of CO2 storage. The position is for a fixed-term
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We are seeking a talented and motivated Postdoctoral Research Associate (PDRA) to join the Energy and Power Research Group for a 12-month appointment to develop, implement and test a numerical model