52 linked-data-"https:" "https:" "https:" "Stanford University" PhD positions at Aalborg University
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decision-making. A main challenge is finding ways to link AI-driven creativity with clear environmental performance feedback early in the design process. This phase is characterized by high uncertainty in
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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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, 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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candidate is expected to publish in leading Human-Computer Interaction venues. Your competencies You hold a master’s degree in human-computer interaction, computer science, interaction design, applied
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(students, staff, and visiting researchers) with sample preparation, measurements, and data analysis Participation in maintaining laboratory safety, including handling of equipment and chemicals Teaching
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of wind turbines. Despite remarkable progress in structural health monitoring boosted by AI, purely data-driven models have no physical interpretability and poor generalization capabilities. Thus
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(LLMs) to explore historical text data and cultural heritage collections. Collections of historical texts are increasingly used to train AI, but, consisting of highly heterogeneous text data
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macroeconomic paradigms. The research will include: Macroeconomic modelling (using SFC and other approaches) Macroeconomic theory covering different paradigms in macroeconomics Integration of financial data
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disturbances or cyberattacks, such as sensor manipulation, electromagnetic interference, or injected faults, can affect the behaviour of power electronic systems. Developing data-driven models that capture how