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digital twin framework, adaptable to: The level of detail available for ship modelling, The quality of risk-related data, and Quantified model and data uncertainties. The project will advance knowledge
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materials, demolition materials as well as renovation materials). Procurement processes are central to create a demand of circular materials, especially how this relates to digitalization, big data management
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research group that views automation engineering as a key enabler for new methods and applications, driving and benefiting from the ongoing digitalization of society. Our research emphasizes social, economic
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of optics, imaging systems, digital holography, speckle metrology, Matlab and simulations, signal processing, the Fourier transform, machine learning, and classifiers such as SVM. Meriting qualifications
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benefiting from the ongoing digitalization of society. Our research emphasizes social, economic, and environmental sustainability. As a postdoc, you will become part of a dynamic team that offers a stimulating
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areas is considered a merit: - Multi-modal data processing for decision-making in natural environments - Terrain classification and multi-layered environment mapping - Digital twin generation for natural
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classification and multi-layered environment mapping - Digital twin generation for natural environments - Semantic scene-understanding in natural environments for robust decision-making - Learning-based
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detailed expositions of particular components (litterfall, soil components), or temporal trends. In addition, model-data intercomparison studies or calibration could be a promising avenue, as
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of real systems, so-called digital twins, are at the heart of the project. The project works with real examples from companies to develop and test innovative solutions. As a postdoc, you will be a senior
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their own research agenda within some of the department’s core themes: Digital transformation and innovation strategy – how organizations adapt their innovation processes and strategic priorities in response