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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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active sites), in vitro and in vivo enzyme screenings, electrochemistry, and machine learning-assisted directed evolution. As part of this project, you will collaborate closely with PhD students and
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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