197 machine-learning-"https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" uni jobs at ETH Zurich in Switzerland
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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
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research at the intersection of law, AI, and social science Are motivated to work with quantitative empirical methods and are willing to acquire skills in experimental and quantitative research Have an
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Armed Forces. The research contributes to the scientific foundation of monitoring and rapid altering systems for underground infrastructure. Project background Seismic and fiber-optic sensing technologies offer
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programming skills in Python Experience with machine learning systems or LLM-based architectures Experience working with complex data systems or developing applied AI prototypes Familiarity with modern AI tools
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80%-100%, Zurich, fixed-term We are looking for a Research Engineer to join ongoing and future research projects at the intersection of machine learning, and structural design (e.g. trusses, space
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benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive pension benefits chevron_right Working, teaching and research at ETH
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to assess ecosystem services Evaluation of plant phenotyping models, jointly with the other Work Packages of PhenoMix Statistical analyses, including machine learning approaches Presentation at national and