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At the Faculty of Medicine, Department of Health Science and Technology, one or more PhD stipends in Human-Machine Interaction are available for appointment from October 1, 2026, or as soon as
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organizational theory, the learning sciences, digital transformation, digital technologies, human-computer interaction, and related fields. Within the specific field, the PhD student will engage in both research
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to understanding and designing AI-supported collaborative learning environments and technologies. The lab investigates how students learn with, through, and around AI, and develops new human-AI-human interaction
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of integrating GenAI in professional design software. You will join the Human Augmentation and Collaboration research group, an active and supportive research group focused on designing and evaluating interactive
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studying how wireless sensing and AI interact in real systems. The work will be carried out in close collaboration with researchers in wireless communications, sensing, machine learning, and robotics, with
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to understanding and designing AI-supported collaborative learning environments and technologies. The lab investigates how students learn with, through, and around AI, and develops new human-AI-human interaction
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of Power-to-X processes for renewable fuel production. The project is primarily rooted in process engineering while also addressing the interaction between Power-to-X plants and the electrical grid. You will
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with human life in its complexity. We ask fundamental questions about life and contribute knowledge about how human beings create meaning, interact, flourish and participate in society. The Department
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of Computer Science and the Department of Chemistry and Bioscience. This stipend is open for appointment from 01.10.2026 (or soon thereafter).The duration of the position is three years. Job Decription Species interact
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be motivated to conduct in-depth empirical research in close interaction with the port sector. Solid qualitative methodological skills are required, and prior experience with qualitative data