45 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" "UNIV" PhD positions at Aalborg University
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creativity and technology, and develops new areas in research and education directed towards the end-user. You can read more about the department here: https://www.create.aau.dk/om-create . How to apply Your
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technology, and develops new areas in research and education directed towards the end-user. You can read more about the department here: https://www.create.aau.dk/om-create . How to apply Your application must
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date. Candidates must be willing to move to Denmark for the duration of the PhD research. Please see the RePIM project website (https://repimnetwork.eu/recruitment/ ) for further information
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AI-driven creativity with clear environmental performance feedback early in the architectural design process. This phase is characterized by high uncertainty in data availability and design parameters
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to evolve classical communication networks to support both traditional data and the unique requirements of quantum information systems (https://www.classique.aau.dk). CLASSIQUE will address a suite of
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. If these problems remain unidentified, they can result in incorrect clinical decisions and poor patient management. In this PhD project, you will collect experimental data describing the changes in blood due to pre
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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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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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using signal changes to learn about the weather and take appropriate action. By combining AI with physics and real-time data, the project improves weather forecasts and makes communication systems more
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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