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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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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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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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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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knowledge and technology from research to Swiss machine, electrical and metal industries. The research group Simulation and Metrology at inspire offers in collaboration with ETH Zurich the following position
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publications. Desired: Experience in nanopore and/or other single-molecule experiments and their interpretation Coding skills for advanced data analysis, machine learning, kinetic modeling, etc. Nanofabrication
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-molecule techniques. Coding skills for data analysis, pattern recognition, machine learning, kinetic modeling, etc. Advanced optics, biochemical wetlab, and/or bioengineering experience. Required: MSc degree
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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