235 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "University of St" "St" "St" uni jobs in Switzerland
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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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component of solid-state transformers (SSTs). Such SSTs are required, for example, in future AI data centres, where power consumption per computer rack increases to levels of several hundred kilowatts or even
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100%, Zurich, fixed-term The Professorship for Research on Learning and Instruction at ETH Zurich strives to discover effective, evidence-based ways to improve educational practice with an eye
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. Profile Applicants must hold a M.Sc. Diploma (120 ECTS points) or equivalent in civil, mechanical or electrical engineering, geosciences, physics, applied mathematics, computer sciences or related fields
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an internationally recognised, competitive research program sustained by external funding. Teaching experience at university level is expected. The candidate will teach at the Bachelor level and contribute
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curious to learn new things, is self-motivated, and has excellent English communication and writing skills. Knowledge of German language will be considered an advantage. Our offer You will be working in a
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a solid platform for the PhD candidate. In line with our values , ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning
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assistant at the chair, you are expected to teach a combination of landscape architectural design and research. You will need to be able to understand the student’s ideas and to help them further developing
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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 chevron_right Working, teaching and research at ETH Zurich We
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deep learning workflows for tree species mapping. The position contributes to building a scalable system for forest monitoring by refining model performance and ensuring high quality geospatial data