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project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD fellow will be part of a growing
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on the development of machine learning algorithms, particularly transfer and adaptive learning, for multimodal wearable biosensing and its translation to rehabilitation and digital health applications. It is co
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Fellow to develop and evaluate artificial intelligence methods for physical medical procedures. The fellow will design and implement machine learning models to analyze procedural data, support clinical
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community partners to ensure outputs reflect local priorities and inform adaptation planning. Duties may include: Develop spatially explicit computational models using machine learning, hydrologic, and energy
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with programming in Python is a requirement Experience with telecentric particle imagers, image analysis, and machine learning for particle recognition is an advantage Experience of working with wave
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with anxiety and co-occurring learning difficulties. Specific duties include recruiting and scheduling participants, making phone calls, assisting with research study visits, collection of cognitive
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, robotics and machine learning, focusing on fundamental aspects as well as on applications in multidisciplinary contexts. This position is part of the DRIVE project, funded by the Research Council of Norway
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques
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will help influence how new evidence drives system change, supporting the region’s shift toward a more integrated, equitable, and learning-oriented model of mental health care. You will be part of
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repair on RNA-DNA hybrids of R-loops using computational modeling and molecular dynamics simulation along with various types of AI tools and machine learning approaches. Temporary Expertise in