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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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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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their Viruses to Assess Peatland Health and Predict Restoration Success - CArBiVoRe”. Specifically, as part of your PhD research, you will explore microbial responses to the restoration of agricultural
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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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production environments, enabling predictive maintenance and data-driven optimization through centralized data platform architectures. Your research will focus on addressing current bottlenecks in data and
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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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using fetal/placental growth as exposure to investigate effects on later health outcomes in mother or child, and risk prediction models. The project will be done in close collaboration with research group
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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