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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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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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functions of single nanoparticles as well of ensembles with varying number of nanoparticles. Advancing the understanding of corporative interactions in nanoparticle catalysis, including ensemble-averaging
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to apply machine learning techniques to a combination of experimental data and simulation results, aiming for faster and more accurate predictions. About us You will join an international and highly
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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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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