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Machine Intelligence (MIRMI) is a globally visible interdisciplinary research center for machine intelligence, which is the integration of robotics, artificial intelligence and perception. Its three central
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of Orthopaedics and Sports Orthopaedics and the Institute for AI and Informatics in Medicine. We work at the intersection of artificial intelligence, medical imaging, and clinical practice, developing methods
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the integration of large-scale biological datasets derived from both the host and the microbiome, employing advanced statistical methods and cutting-edge artificial intelligence techniques to uncover novel insights
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for practitioners. Your responsibilities include: Science-based research on the disarmament and non-proliferation of chemical weapons, e.g. on applications of artificial intelligence (AI). Monitoring, tracking and
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the central hypothesis of these infections causing general stress responses and behavioural changes, which can be assessed on metabolic level or by video observation and artificial intelligence analysis
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for complex geochemical systems, including deep sea hydrothermal vent environments of geothermal fluids. This project bridges modern artificial intelligence with geochemical modelling, aiming to deliver
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understanding of intersectoral scientific fields ranging from optical fibre communication design and implementation, components fabrication, artificial intelligence, or techno-economic analysis, which represent
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, employing advanced statistical methods and cutting-edge artificial intelligence techniques to uncover novel insights into these complex interactions. As part of this position, you will have the opportunity
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Research Assistant (m/f/d) in the field of Theoretical Ecology and Evolution or Computational Biolog
-based modeling b) Modeling with differential equations c) Modeling of metabolic networks and interaction networks d) Deep learning and artificial intelligence Acquisition of third-party funding
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determination Experience in machine learning or artificial intelligence, for example, pattern recognition or neural networks Experience in analyzing large data sets Previous knowledge of climate or ocean modeling