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, or HCI methods familiarity with adaptive systems or machine learning prior experience conducting user studies Beneficial background in computational interaction or adaptive systes knowledge of optimization
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20%-40%, Zurich, fixed-term The Public Policy Group at ETH Zurich invites applications for a research assistant in quantitative social science for a project using machine learning to improve refugee
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system design. We employ advanced computational methods, machine learning, modeling, and custom hardware and software to continually test our solutions in various real-world industry projects. In one
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Mixed Reality. This research combines physiological time series analysis (such as or similar to EEG, EMG, EOG), machine learning, and real-time system design for intelligent interaction systems
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statistical evaluation Machine learning analyses: implementation of established and new workflows Coordination of activities with Consortium partners, including presentation of results at consortium meetings
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main areas of research are machine learning, distributed systems, and the theory of networks. Within these three areas, we are currently working on several projects: graph neural networks, natural
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members of the Computational Mechanics Group and develop it further from both the theoretical and the computational point of view. Job description You will have the unique opportunity to learn, develop and
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understanding and practical experience with machine learning approaches for biomarker discovery and predictive modeling, specifically with hands-on experience in developing and applying neuronal networks
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experience CH/EU/EFTA citizenship or a valid Swiss work permit Master’s degree (ETH, university) in engineering, mechatronics, computer science, or a related field Experience in machine learning, signal
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informatics, especially chronic disease management (with emphasis on asthma) Machine learning / deep learning / artificial intelligence Profile Ideally, the candidate has a degree in Electrical Engineering and