22 parallel-processing-"Computer-Vision-Center" PhD positions at Aalborg University in Denmark
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PhD position in Clinical Natural Language Processing Apply Vacant position PhD position in Clinical Natural Language Processing Aalborg Deadline : 02.11.2026 Department: Department of Clinical
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both the communications and AI communities. Your competencies We are looking for a candidate with a Master's degree in electrical engineering, computer engineering, computer science, data
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has: MSc degree in Geodesy, Hydrology, Water Resources, Civil Engineering, Physics and Meteorology, Applied Mathematics, Computer Science/Engineering, or a comparable discipline. The MSc degree must be
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design the next generation of circular plastics systems by combining hands-on polymer processing with data-driven modelling. The PhD study is a full-time, fixed-term position of three years, including
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Computer Science Ref number: 2026/958 Employment type: Full Time Apply Aalborg Deadline : 31.08.2026 Department: Department of Computer Science Ref number: 2026/958 Employment type: Full Time Apply Vacant
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classification. You will evaluate the detection of accelerants, contaminants, and degraded forensic evidence and integrate advanced data processing and visualisation techniques to handle and interpret large
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A PhD stipend within the AI:EcoNet Lab (DEIS) Apply Vacant position A PhD stipend within the AI:EcoNet Lab (DEIS) Aalborg Deadline : 16.08.2026 Department: Department of Computer Science Ref number
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Computer Science Ref number: 2026/1150 Employment type: Full Time Apply Aalborg Deadline : 13.11.2026 Department: Department of Computer Science Ref number: 2026/1150 Employment type: Full Time Apply
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develops new human-AI-human interaction paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported
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degree Programming skills (Python) and experience with common machine learning platforms Experience with deep learning, computer vision, medical image analysis or unsupervised learning is an advantage