38 data-"https:"-"https:"-"https:"-"https:"-"Universidade-do-Minho" PhD positions at Aalborg University in Denmark
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with a total of 18 PhD stipends. The AI:HealthData Lab is part of the AI:X initiative with two PhD stipends and is a collaboration between the Data Engineering, Science, and Systems (DESS) research group
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. More information about DESS can be found at: https://www.cs.aau.dk/research/Data-Engineering-Science-and-Systems. The Center for Classical Communication in the Quantum Era (CLASSIQUE) is a pioneering
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at the Copenhagen campus and one at the Aalborg campus. The themes cover key research areas of the department. Stipend no. 3: Data-driven methods for design and operation of human-centric energy-optimized indoor
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campus and one at the Aalborg campus. The themes cover key research areas of the department. Stipend: Synthetic Relighting of Real-World Environments via Generative AI and Computer Graphics Pipelines
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. More information about DESS can be found at: https://www.cs.aau.dk/research/Data-Engineering-Science-and-Systems How to apply Your application must include the following: o Application, stating reasons
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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
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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