232 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions in Switzerland
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knowledge and technology from research to Swiss machine, electrical and metal industries. The research group Laser Material Processing at inspire offers in collaboration with the Advanced Manufacturing
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methods. Contributing to the development, adaptation, and application of machine‑learning models tailored to RODI data (in collaboration with project partners). Designing and implementing an innovative
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upcoming areas off the beaten paths. Our three main areas of research are machine learning, distributed systems, and theory of networks. Within these three areas, we are currently working on several projects
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space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where conventional
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. • Familiarity with machine learning, dimensionality reduction, clustering, and statistical modeling. • Strong communication skills, interest in interdisciplinary work, and ability to train students and postdocs.
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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 the ASVZ , childcare and attractive
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with the Sinergia project partners You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive
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we seek to enhance resources for student learning on statistics and machine learning applied to these topics. Project background We would like to develop learning exercises that help students learn how
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stable water isotope data, and Statistical analyses, including machine learning approaches. The full-time position is funded for four years. Salary and social benefits are provided according to ETH Zurich
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modeling of historical controls, as well as machine learning, data science, and epidemiological studies based on large SCI datasets. This is an excellent opportunity to contribute to translational research