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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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restraint conditions. A key goal is to develop both a sensor system and a prediction model for the short- and long-term deformation behaviour of concrete. These tools will be applied to full-scale structural
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involving protein structure prediction by AlphaFold 3 combined with crosslinking/mass-spectrometry and single particle cryogenic electron microscopy on native or recombinant TZ complexes, you are expected
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) therapy on the biology of γδ T cells and how can we use this knowledge to help us predict the success of therapy and prevent the development of side-effects. Position 1 will focus on the cellular and
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measured data, apply necessary filtering and selection of data features to be stored. Couple the numerical model and the measured input data to establish a model that can predict the outcome in terms
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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 areas of responsibility include: Develop computer vision and AI models for detecting wind turbine blade damage and predicting its progression, with experimental validation carried out at DTU test
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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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research in the field of modern high voltage polymer electrolytic capacitors, develop models for lifetime prediction, methods to predict and test for reliability, understand physics of failures at elevated
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employing an integrative approach involving protein structure prediction by AlphaFold 3 combined with crosslinking/mass-spectrometry and single particle cryogenic electron microscopy on native or recombinant