193 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S"-"University-of-St" positions at ETH Zurich
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and professionals across emerging areas like machine learning, cyber security, climate risk, distributed ledger technology, and quantum computing and translates that expertise into integrative research
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innovative methods to leverage machine learning for numerical weather forecasting and climate modeling. Project background We are looking for a motivated Machine Learning Scientist to join the development team
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of statistical seismology and machine learning. This position is part of two EU-funded projects: GeoTwins , which focuses on creating digital twins for geothermal systems, and Earth-AID, which aims to develop
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100%, Locarno, fixed-term Recent advances in AI-based weather prediction have demonstrated remarkable skill and computational efficiency. However, most current machine-learning weather prediction
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innovative methods to leverage machine learning for numerical weather forecasting and climate modeling. We are looking for a motivated Machine Learning Scientist to join the WeatherGenerator project. Like
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” at ETH Zurich. The project develops and evaluates AI-based prototypes to support judicial decision-making, with a focus on real-world applicability. It combines machine learning, legal applications, and
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100%, Zurich, fixed-term The postdoctoral researcher will advance the application of AI, large language models (LLMs), and machine learning to extract trustworthy climate information from large
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interested in applied machine learning and computer vision at the intersection of research and industrial deployment. Job description Develop and implement state-of-the-art computer vision algorithms
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Center for Project-Based Learning. The successful candidate will contribute to research at the intersection of embedded machine learning, signal processing, and smart sensing systems, with applications in
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Thermal effects are a major source of geometric errors in modern machine tools. Accurate prediction of temperature fields inside machine structures is therefore essential for improving machining