132 data "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at Aalborg University in Denmark
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on development of wave energy and offshore wind. Most of our research is focused around work in our wave flume and basin. Further description of the group may be found here: https://vbn.aau.dk/en/organisations
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research on architectures and methods for the real-time delivery of EO data from dense nanosatellite/CubeSat constellations and to develop innovative GNSS-based sensing methods and AI models to detect a
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(such as heart disease, diabetes, and cancer) using, for example, data from registries and/or biobanks. The research will be performed in close collaboration with Center for Clinical Data Science (CLINDA
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University (AAU) in Aalborg. This full-time position offers a unique opportunity to contribute to high-impact research in drones, AI, and human-computer interaction. Successful candidates will join a dynamic
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the project based on your interests and in collaboration with a leading architectural firm. The candidate is expected to publish in leading Human-Computer Interaction venues. Your competencies You hold a PhD
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Communication, the Faculty of Social Sciences and Humanities and the Center for Clinical Data Science (CLINDA), Department of Clinical Medicine, the Faculty of Medicine. AI:GENE-XPLAIN develops AI tools
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optimization of production systems and supply chains, including digital twins, virtual system validation, process modeling, and data-integrated decision models. Research should explicitly support managerial
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experimental methods such as viromics and metatranscriptomics. The data will be linked to soil and emission data to help create predictive models. Within a broader framework, your work will contribute
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, the spatial and temporal resolution of EO data. MASSIV-EO aims to overcome these limitations through foundational research on architectures and methods for the real-time delivery of EO data from dense
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) Data-driven and AI-assisted methods for power electronics Across the above areas, you are expected to contribute to model-based and data-driven/AI-based methods, including digital twins, physics-informed