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for optimizing metals microstructures in-situ during the AM process as well as ex-situ during post-AM treatments and enable predictions of the microstructural evolution, and thus changes in properties, while AM
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sustainable materials, (d) Artificial Intelligence (AI) models to predict and control the manufacturing process and (e) a Digital Twin (DT) incl. Building Information Modeling (BIM) information backbone
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platforms can unify production environments, enabling predictive maintenance and data-driven optimization through centralized data platform architectures. Your research will focus on addressing current
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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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) 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 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 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