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, collaboratively developing your own research line and publishing in leading machine learning conferences (e.g., NeurIPS, ICLR, ICML) and scientific journals; Actively build bridges with experimental groups within
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to obtain this; You are proactive in developing research collaborations and have the ambition to acquire external funding in interdisciplinary settings; You have excellent communication skills and are fluent
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approaches combining control, learning, and uncertainty quantification. This project develops a data-driven control framework grounded in first-principles models with emphasis on: Data-driven practical
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causal inference, machine learning, text analysis, or large-scale data integration. You support ODISSEI users via consultations and collaborative research, train researchers through workshops, and mentor
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and democratic participation of citizens. You will focus on developing adaptive learning systems that enhance the transparency and contestability of AI decisions through personalized, multimodal
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machine learning libraries. Familiarity with collaborative coding environments (e.g., Git) and working on high-performance computing (HPC) clusters is an advantage. Good scientific writing and communication
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organs, medical imaging, and machine learning-driven clinical decision support. Close collaboration with clinical partners and industry ensures that innovations are translated into real-world healthcare
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participation of citizens. You will focus on developing adaptive learning systems that enhance the transparency and contestability of AI decisions through personalized, multimodal explanations. Your job AI is
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industrial partners. Our group offers an open and collaborative environment in which we focus on hands-on learning and personal growth of all group members. We are looking for excited and talented candidates
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-physical systems secure and resilient in the presence of uncertainty and cyber-physical attacks? Then you may be our next PhD candidate in resilient and learning-based control of cyber-physical systems