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an interdisciplinary project briding Environment, Supply Chain, Public Policies, and Machine Learning. Project background This position will be to support one joint project with UNEP, focusing on streamlining
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. Atmospheric transport is known to mediate the exchange of microorganisms within and between terrestrial and marine environments. However, airborne microbial communities remain the least studied microbiome on
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predict building footprints and heights in Chinese cities. We offer Standard ETH student RA salary. An interdisciplinary team with good support and a friendly learning environment. A possibility to work
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. You can look forward to an exciting working environment, cultural diversity and attractive offers and benefits. Your profile You are a Master’s student in Computer science or in a relevant domain. This
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with high precision. It is widely used to reveal the electronic environment of paramagnetic defects in dielectric crystals as well as to manipulate the electron and nuclear spin states of solid-state and
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look forward to an exciting working environment, cultural diversity and attractive offers and benefits. Your profile You should hold a PhD in biochemistry or molecular biology with demonstrated expertise
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researcher with a strong machine learning background. You should be interested in working in an interdisciplinary environment at the interface of Machine Learning, Medicine and Health Care. Job description You
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to an exciting working environment, cultural diversity and attractive offers and benefits. Your profile You will have strong quantitative skills, and a background in either physics, biophysics, bioengineering
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environment in a world-class research institution; Flexible work arrangements, including remote working, flexible time, condensed week, and the opportunity to work part-time; Exciting challenges, varied
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well as in vivo models of disease. Job description You show a collaborative mindset, work independently and with precision in a fast-paced environment. This position offers the opportunity to Run multiple