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to achieve them. Acquire new knowledge quickly and use existing knowledge in new ways. Work constructively under pressure or in the face of adversity. Demonstrate strong problem-solving abilities with a
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an advantage: Good knowledge of mathematical optimization (e.g., mixed-integer linear programming, stochastic programming, meta-heuristics) Knowledge of machine learning and/or AI methods Experience with
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global institutions. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including early-stage researchers eager to contribute to this emerging
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team includes: Prof. Ivan Depina – main supervisor and coordinator, probabilistic modelling, scientific machine learning Prof. Mohamed Hamdy – building performance simulation, building automation
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analyses or game theoretic analysis. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. The evaluation of
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): · Hybrid Multiscale Modelling · Hybrid Physics-Machine Learning Analyses · Coupled Atmosphere-Turbine Models · Uncertainty Quantification in Hybrid Frameworks We offer the opportunity to work in a very
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the motivation is that design and operational data, gathered across many vessels and many projects, could form one coherent body of knowledge that AI methods can search and learn from, helping surface relevant
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position This PhD project is connected to FME NorthWind (https
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system for ferry quays, containing labelled damage data as training material for a machine learning model capable of automatically classifying structural damage from drone inspection images. The project
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Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. ... (Video unable