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-learning models on resource-constrained hardware Candidates should also have experience or strong interest in Edge AI, TinyML, embedded machine learning, neuromorphic computing, or signal processing. We
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, earth and planetary scientists, chemists and biologists to understand the conditions and the mechanisms that enable life to emerge. Developing reliable methods to detect reliable traces of life
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software, and fault-management functions. Integrate flight software with onboard computers, sensors, actuators, payload electronics, communication interfaces, ground systems, and externally supplied
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The Center for Security Studies (CSS) at ETH Zurich is a center of competence for Swiss and international security policy. It offers security policy expertise in research, teaching, and consulting activities. Curious? So are we. We look forward to receiving your online application, by October...
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, establishing on-site monitoring methods and testing possible treatments for it. The selected student will collaborate with another PhD candidate funded in the scheme and employed at the Chair for Digital
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of knowledge and practices. You are welcome to further develop transdisciplinary methods and theories and to make your achievements known via talks and publications. The position is for three years initially and
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The PhD will form part of a broader interdisciplinary research programme in mobile health and AI, with applications in areas such as mental well-being, fall prevention, stroke rehabilitation, and
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The Swiss National Supercomputing Centre (CSCS) develops and operates a high-performance computing and data research infrastructure that supports world-class science in Switzerland. Its user
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This Senior Specialist supports the Vice President in advancing ETH Zürich's priorities in artificial intelligence, digital transformation, innovation, and emerging technologies, with particular responsibility for stakeholder engagement, project coordination and communications. This role...
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and Climate Science at ETH Zurich and the WSL-Institute for Snow and Avalanche Research SLF and will be part of the NCCR CLIM+ programme which is funded by the Swiss National Science Foundation (SNSF