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paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported Collaborative Learning (CSCL), Computer-Supported
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information, please contact GEUS Senior Researcher Anja Rutishauser, e-mail: [email protected] telephone: +45 9133 3404, GEUS Head of Department Signe B. Andersen, email: [email protected] telephone: +45 9133 3804
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Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position
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paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported Collaborative Learning (CSCL), Computer-Supported
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the AI:X initiative with two PhD stipends (one of which has already been filled) and is a collaboration between the Department of Electronic Systems, the Technical Faculty of IT & Design and the Department
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related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with deep learning, computer vision, medical image analysis
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deep learning, computer vision, medical image analysis or unsupervised learning is an advantage. English language skills, both written and spoken Qualification requirements PhD stipends are allocated
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or Computer Science; Human-computer Interaction, Spatial Cognition or related areas; Engineering, Applied Mathematics, Statistics or another Quantitatively Oriented Discipline. Application procedure Your complete
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organizational theory, the learning sciences, digital transformation, digital technologies, human-computer interaction, and related fields. Within the specific field, the PhD student will engage in both research
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Engineering, Machine Learning, Applied Mathematics, or a related field. A strong academic background and interest in AI systems, embedded intelligence, edge computing, machine learning, or related areas. Strong