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partners from news media and media technology research topics like media experiences and trust, recommender technology, content production and analysis, content interaction and availability, and Norwegian
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diagnostics based on Terahertz spectroscopy.The work will involve a rich variety of topics encompassing plasma/material interactions studies, material/plasma reactor optimization, as well as the design and
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transparent and intelligible. Although explainable AI methods can shed some light on the inner workings of black-box machine learning models such as deep neural networks, they have severe drawbacks and
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studies on intelligent transportation systems suggest that the coordination of vehicles in traffic constitutes an important aspect of urban planning, generating significant savings and improving safety in
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years, practitioners and researchers have realized that predictions made by machine learning models should be transparent and intelligible. Although explainable AI methods can shed some light on the inner
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to human and environmental interactions across various sectors such as healthcare, education, and urban planning. The primary aim of this project is to develop Multi-Intelligence Agents (MIAs) that combine
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framework of responsible human-AI collaboration to better understand the role of human oversight in bias mitigation. Understanding human needs for bias mitigation – Understanding how individuals interact with
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of the Data and Artificial Intelligence unit at the Department of Computer Science. About the project The position is funded by the Norwegian Research Center for AI Innovation - NorwAI - and will be based
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readiness levels. Additionally, your work will directly support the FAIR-AI flagship project for the Austrian Artificial Intelligence Mission AIM 2030, ensuring your research generalises and gains high
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In cooperation with Max Planck Institute for Biological Intelligence Ludwig-Maximilians-Universität München Technische Universität München Teaching language English Languages The language of