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, resulting in inconsistencies across soil properties and underperformance in data-scarce regions. This PhD project will develop next-generation machine learning methods for geospatial prediction by integrating
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& Spatial Planning, Physical Geography, and Sustainable Development. The team of the Department of Physical Geography excels in research and education on BSc, MSc and PhD level. We research processes
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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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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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professors, two postdocs, and five PhD-students. The group focus on high-quality applied research. The current topics of interest in the group include student learning, transitions and career, teacher
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Profile areas Cluster of Excellence CMFI Cluster of Excellence GreenRobust Cluster of Excellence HUMAN ORIGINS Cluster of Excellence iFIT Cluster of Excellence Machine Learning Cluster of Excellence TERRA