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Field
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control with teleoperated human inputs? Change: Develop novel algorithms and interfaces for teaching robots in shared control with human operators. Impact: Provide a seamless interface for humans to teach
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-based, probabilistic, and in-memory computing, are based on a wide variety of physical processes, materials, architectures, and algorithms. For effective implementation, these aspects need to be mapped
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investigating astrocyte biology at multiple scales, from molecules to whole-brain function. As such, you will collaborate with an enthusiastic team of PhD students and postdocs from the lab of Mark Verheijen, as
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benefits resulting from changes of this system. Inventorizing its multiple functions, actors and users of the port water system, she/he/they will place these perceptions within the context of the broader
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– its meaning and potential future risks and benefits resulting from changes of this system. Inventorizing its multiple functions, actors and users of the port water system, she/he/they will place these
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ability to effectively deal with external developments such as staff shortage. This will be based on insights into the characteristics of operations, and attributes (e.g., learning demand, availability
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of the University of Groningen in The Netherlands. The position is available within the research project "Aggregating Safety Prefer-ences for AI Systems: A Social Choice Approach." The project aims to develop formal
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since early days of the industrial revolution. Particularly novel separation methods for products from electrochemical synthesis routes need to be developed & demonstrated at pilot scale for industrial
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across domains. The research unit Intelligent Systems (IS) in Computer Science is focused on the development of Data Science, Pattern Recognition and Machine Learning algorithms for interdisciplinary data
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across domains. The research unit Intelligent Systems (IS) in Computer Science is focused on the development of Data Science, Pattern Recognition and Machine Learning algorithms for interdisciplinary data