42 information-technology "https:" "https:" "https:" "https:" positions at ETH Zürich
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diversity, and is responsive to the needs of dual-career couples and care responsibilities. In your application, please refer to myScience and reference JobID 70453. Where to apply Website https
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18 Sep 2026 Job Information Organisation/Company ETH Zürich Research Field Computer science » Other Engineering » Electrical engineering Engineering » Other Researcher Profile Recognised Researcher
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18 Sep 2026 Job Information Organisation/Company ETH Zürich Research Field Chemistry » Other Environmental science » Earth science Environmental science » Other Researcher Profile First Stage
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12 Sep 2026 Job Information Organisation/Company ETH Zürich Research Field Engineering » Aerospace engineering Engineering » Civil engineering Engineering » Materials engineering Engineering
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22 Aug 2026 Job Information Organisation/Company ETH Zürich Research Field Engineering » Other Environmental science » Earth science Researcher Profile First Stage Researcher (R1) Application
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Department of Health Sciences and Technology (www.hest.ethz.ch ) at ETH
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the field of energy conversion and storage across scales, driven by fundamental challenges and with the aim of resulting in technologies for societally relevant applications. She/he is expected to establish
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programme focused on the development, analysis, and implementation of cutting‑edge Machine Learning and AI methods for applications across the sciences and engineering. The successful candidate is expected
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– shaping innovation with excellence, responsibility, and impact. The Departments of Computer Science (D-INFK; www.inf.ethz.ch ) and Information Technology and Electrical Engineering (D-ITET; www.ee.ethz.ch
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of computational and applied mathematics, including but not limited to data-driven numerical modeling, scientific machine learning and AI for science and engineering, computational uncertainty quantification