357 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" positions in Switzerland
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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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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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), 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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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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these exclusively via our job portal. Applications by e-mail and by post will not be considered. Where to apply Website https://academicpositions.com/ad/empa/2026/chemist-physicist-or-environmental-s… Requirements
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between behavioral and computer scientists. The ideal candidate has some knowledge in both areas, and the specific behavioral domain is open to discussion. Project B – Understanding and Countering
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Opportunities to learn cutting edge techniques Perspectives for career development A diverse and interdisciplinary team Working, teaching and research at ETH Zurich We value diversity and sustainabilityIn line
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competitive grant proposals to support independent and joint research activities. Where to apply Website https://apply.refline.ch/673278/3852/pub/en/index.html Requirements Research FieldPhysics » Computational
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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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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