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, leveraging advanced machine learning to combine these diverse data sources. By identifying the most informative clinical features, the approach seeks to provide more accurate and interpretable recovery
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work on designing novel Smart Sensors & Energy Efficient Machine Learning on Microcontrollers. The objectives of this thesis include: Design and prototype modular, low-cost sensor nodes integrating
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100%, Zurich, fixed-term We have an open PhD position at the intersection of machine learning, embedded intelligence and human–computer interaction. The project will explore how learning systems can
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methods in learning sciences and educational research You are preferably studying Computer Science or a related field You are interested in Learning Sciences or Human-Computer Interaction (HCI) You are
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tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive pension benefits chevron_right Working, teaching and research at ETH Zurich We value diversity and
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(preferably Python), multiple years of programming experience as well as profound knowledge of professional computer-aided design and 3D modelling In addition, you have experience in CAD/CAM (preferably McNeel
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Master’s students of national and international institutions Strong motivation to explore topics in human–computer interaction, learning technologies, and AI-assisted tools Experience or strong interest in
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background in AI/ML and experience with Python-based machine learning frameworks. A solid understanding of Linux systems and containerized deployment within Kubernetes is a strong asset. The candidate should
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models at process scales, the project combines efficient mathematical concepts like automatic differentiation with backpropagation – the same concept that powers machine learning and artificial
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machine learning models, and develop a generalizable decision-support system for vehicle and station allocations. This research will be conducted together with domain experts and collaborators. The research