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learning is a crucial concept in the fields of machine learning and pattern recognition, because it enables the discovery and representation of underlying structures and patterns in high-dimensional data. In
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. The position is open to either an incoming or an outgoing candidate, see LEAD AI mobility rules About the host research group and research theme Manifold learning is a crucial concept in the fields of machine
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scientific environment, consisting of academic staff members, Ph.D./Postdoctoral fellows, and students with scientific expertise ranging from theoretical methods (machine learning, biostatistics
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specific research questions. While the group has started to apply machine learning tools to a number of data sets, the seismic network still operates based on conventional tools. Objectives of this position
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. Amongst others, the group's current projects leverage approaches from network science and machine learning in tool development for (1) modeling of regulatory interactions at bulk and single-cell resolution
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deployments from various areas to address specific research questions. While the group has started to apply machine learning tools to a number of data sets, the seismic network still operates based
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(BCCR). The position is open to an incoming candidate, see LEAD AI mobility rules About the host research group and research theme Current data-driven Artificial Intelligence – Machine Learning (AI-ML
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) that employ artificial intelligence (AI). Central AI techniques for us are deep learning, image analysis, knowledge graphs, machine learning, and natural-language understanding. We investigate
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Current data-driven Artificial Intelligence – Machine Learning (AI-ML) approaches to assess extreme weather and climate events struggle to cope with events outside their training regimes (such as
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techniques for us are deep learning, image analysis, knowledge graphs, machine learning, and natural-language understanding. We investigate application areas such as digital multimedia forensics, decision