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combines multimodal data sources, physical models, and advanced machine learning to create new forecasting and communication tools. The lab is looking for candidates for the following two stipends: Stipend 1
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(DInSAR). Minute surface uplift and subsidence signals will be automatically detected using machine-learning workflows, enabling systematic, user-independent identification of drainage events every 6–12
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If you want to pursue a research career at the intersection of additive manufacturing (AM), microstructural engineering and advanced statistical/machine-learning (ML) based modelling, then this PhD
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Disentanglement of the Exceptional Biological Learning Machine, which is headed by Professor Jan Østergaard. The goal is to develop novel information-theoretic methods for identifying and analyzing temporal and
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
close collaboration with a specific group (DARSA) specialized in developing and applying remote-sensing tools and innovative open-source machine-learning methods. Key responsibilities Develop effective
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Research Assistant in Physical Computing and Wearables at the Department of Computer Science, Aar...
research is at the cutting edge of Human-Computer Interaction (HCI), personal fabrication, and physical user interfaces. As a research assistant, you will support our research team on implementing a novel
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leading Human-Computer Interaction venues. Your competencies You hold a master’s degree in human-computer interaction, computer science, interaction design, applied artificial intelligence, architectural
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process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support process and
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close collaboration with a specific group (DARSA) specialized in developing and applying remote-sensing tools and innovative open-source machine-learning methods. Key responsibilities Develop effective
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knowledge of process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support