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PhD Knowledge-Infused AI for Human-Technology Integration (V24.0004) « Back to the overview Job description Synergies between humans and AI-enabled systems are relevant in situations involving
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-sectional and longitudinal econometrics and machine learning techniques to determine the relationship between environmental stressors and population health. This project is part of a larger project ECOTIP
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PhD position on the topic The Relationship between Health and Environment (V24.0118) « Back to the overview Job description Project description While the association of adverse environmental
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addition, it will employ cross-sectional and longitudinal econometrics and machine learning techniques to determine the relationship between environmental stressors and population health. This project is
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PhD position on Human-Centred Digital Twins in Industrial Work Environments (V24.0003) « Back to the overview Job description Digital twins enjoy significant take-up in a wide range of industries
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Challenge: Human Behavior Recognition and Generation with Large Contextural Behavior Model Change: Incorporate context for human behavior recogintion and generation Impact: Advanced techniques
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concerns, which can be observed in concerns about surveillance capitalism, instrumentalization of production and consumption, datafication of all domains of human and non-human life etc. We can frame these
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of all domains of human and non-human life etc. We can frame these concerns in terms of the social disruption of the lifeworld – i.e. the meaningful environment of everyday life experience in which we
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designed and deployed in ways that align with values such as transparency, explainability, and trust. This research will delve into the intricacies of AI-human collaboration in high-stakes environments
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learning analytics-based dashboards at the Human-Technology Interaction Group (HTI) within the School of Innovation Sciences. The project Blended learning has become a significant component of the modern