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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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is to leverage advanced machine learning to develop an automated design process of mechanical walking aids, analyse gait patterns, and make biomechanical simulations embedded in the generative
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of computationally efficient data-driven control and machine learning methods that enable deployment on edge devices with limited computation. We are looking for a motivated doctoral student to contribute
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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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to machine learning, AI, and industrialization. With a large multidisciplinary team of professionals across three locations (Lausanne, Zurich, Villigen), the SDSC provides expertise and services to various
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professional development, but also actively contributes to positive change in society You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by
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background in quantitative methods and experience in analyzing secondary data Experience of working in low- and lower-middle-income countries and/or experience of statistical learning methods would be
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contribute to diverse machine learning projects across ETH's research and administrative domains, developing and implementing scientific computing solutions to support various projects. Throughout all your
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, such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive pension benefits The Mucosal Crosstalk group of Dr. Annika Hausmann (SNSF