344 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" positions in Switzerland
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the Collaborative Doctoral Partnerships programme, training researchers at the science-policy interface. Where to apply Website https://jobs.unibas.ch/offene-stellen/phd-position-ai-driven-pathways-to-health
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to supply a letter upon request. Application deadline: 30 November 2025. Applications should be uploaded to the EPFL recruitment page: https://facultyrecruiting.epfl.ch/position/60817413 Inquiries can
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with competitive salary according to ETH standards Interdisciplinary and international research environment You can expect numerous benefits , such as public transport season tickets and car sharing, a
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and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure
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beamline Publish results in scientific journals and present findings at international conferences Where to apply Website https://apply.refline.ch/673278/3854/EL6dzSlLCLf7qHjFYjOSmL8NTV0nuikWIOXeIbXpRu
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better learning and reasoning over data. We are looking for a Postdoctoral Researcher for an initial project of 24 months (extendable depending on funds). In partnership with a leading company in the field
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), INSERM (FR), University Utrecht (NL), SciCross (SE), RD –Néphrologie SAS (FR), University of Bern (CH). For more information https://www.cordis.europa.eu/project/id/101225380 Your Research Environment In
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systems, and space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where
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interdisciplinary research community at the EPFL School of Life Sciences fosters interactions with allied disciplines on campus, including engineering, physics, chemistry and computer sciences. EPFL offers an English
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combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures