194 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" uni jobs at ETH Zurich
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energy transfer, developing and employing computer simulations, laboratory experiments, and field analyses. Our aim is to gain fundamental insights and develop sustainable technologies to address societal
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. Joël Mesot. The closing date for applications is 22 February 2026. We are not accepting applications for this job through MathJobs.Org right now. Please apply at https://ethz.ch/en/the-eth-zurich/working
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environment where philosophy meets data science, public health, medicine, and law? At the Health Ethics & Policy Lab (https://bioethics.ethz.ch ), you will join a team committed to shaping responsible
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understanding of the aging of solid insulation under mixed-frequency medium-voltage stress, see https://doi.org/10.1088/1361-6463/acd55f for a relevant example research work of our team in this area. Profile
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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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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