203 data-"https:" "https:" "https:" "https:" "U.S" "UCL" "UCL" positions at ETH Zurich
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, the Swiss Data Science Center, and Agroscope. PhenoMix focuses on legume-cereal mixtures, i.e., different varieties of pea and lentil as well as wheat, oats, and barley grown under an experimental field
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this role, you will design and implement AI architectures that combine industrial data, domain knowledge, and modern language models. You will collaborate with leading Swiss industrial partners and focus
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100%, Zurich, fixed-term If you are intrigued by study of past climate, enjoy having your hands on samples in the lab and generating new geochemical data, and are a quantitative thinker, you might
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of the Varda machine learning weather prediction system. The model is being trained using archive data from MeteoSwiss operational forecasts and observations, with the objective to provide accurate and fast
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reliable ground truth data for developing automated condition indicators from GPR measurements. Job description Support in planning and preparation of the experimental campaign (test track configuration
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the genomics data, and study the fate and impact of bioaerosol. Bioaerosol, including airborne bacteria, viruses, fungi, and fragments of these organisms pose risks to human. Emerging hazardous components
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relevant information from the papers. Our research group is dedicated to investigating human cognition and learning processes. We conduct theory-driven research about how people learn and how to develop
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postdoctoral researcher with a strong background in sequence bioinformatics, algorithms and data structures. The successful candidate will join an interdisciplinary effort developing innovative diagnostic
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motivated individual to develop Python tools for analyzing data from our holographic cloud particle imagers. Our research group studies the crucial role of clouds and aerosols in the climate system, aiming
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Computer Vision and Computer Graphics techniques to digitize human avatars and garments in 3D. Within this project, your role is to advance our existing algorithms that reconstruct 3D garments from multi