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to the development of a multi-component framework for reliable and computationally efficient fatigue diagnosis and prognosis of steel structures. Building on the group's established expertise in virtual sensing and
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to the development of a multi-component framework for reliable and computationally efficient fatigue diagnosis and prognosis of steel structures. Building on the group's established expertise in virtual sensing and
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a particular focus on the integration of artificial intelligence with wireless sensing and communication. The research will study how radio signals and multimodal sensor observations can be jointly
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or more stays at the collaborating partners in Gothenburg of a few weeks to a few months, and close collaboration with both UCPH-based and visiting PhD students and postdocs. Our group and research – and
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, using linear programming methods. Over the next years, the project will grow to a collaborate team of 4-5 PhD students and Postdocs. The successful candidate will work directly with the project's
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transportation system which include compression and liquefaction. The impurities will pose a safety and lifetime estimation risk to the system as they are very corrosive. There is a need to expand the knowledge
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maintain and develop the operational system around the National Hydrological Model for real-time data generation as well as integrating new data sources such as remote sensing. A central aspect of the role
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systems and edge intelligence. Advanced Architectures & Edge AI: Familiarity with modern neural networks is required. Experience with edge-specific model compression—such as knowledge distillation
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part of the interdisciplinary Novo Nordisk Fonden national project housed at CED, you will work in collaboration with other PhD students and a postdoc, and will have ample opportunities to interact with
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with Imperial College London and the new DTU Smart Sensing Lab. The project is supervised by Associate Professor Rico Krueger and co-supervised by Associate Professor Carlos L. Azevedo at DTU, with