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learning, you will help develop new methods for understanding complex failure mechanisms—an area where existing industrial knowledge remains limited. The project will be executed in three systematic phases
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derived use cases by focusing on one or more of the following topics in their PhD project: Training and inference of ML models on GPU clusters. Method development for scalable and green AI. Use cases in
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deformation. Responsibilities Develop scientific machine learning methods in close collaboration with team members specializing in experimental techniques and materials science. Utilize unique experimental data
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testing dynamic equivalencing methods for power system dynamic simulations and integrating these into commercial simulation tools. Dynamic equivalents are simplified representations of complex power system
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both. Mathematical maturity and experience with scientific programming are essential. A background in probabilistic methods is highly desirable, at the level of master’s courses in probability
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grasp of spatial statistical methods in R is considered highly advantageous. Excellent communication skills are required, with proficiency in English and preferably one of the Scandinavian languages
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for research and innovation in the area of food allergenicity prediction. Responsibilities and qualifications In this PhD project you will contribute to the development of innovative methods allowing
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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution
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the project will be the development of physics enhanced data driven methods to achieve reliable prediction of residual usable life of milling tools. The approach will be validated by application to industrial
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the Computer Science study program. The stipend is open for appointment from August 1st 2025 or soon thereafter. The PhD students will be working on topics within the general areas of formal methods, model checking and