197 evolution-"https:"-"https:"-"https:"-"Goethe-University" positions in Switzerland
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are expected Workplace Workplace We offer Your job with impact: Become part of ETH Zurich, which not only supports your professional development, but also actively contributes to positive change in society You
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scope to influence the long-term development of ECAF’s analytical capabilities, workflows, and user support model. A collaborative environment with a broad scientific user community. Employment conditions
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complex, dynamic, and multi-functional behaviors through their billions of years of evolution. Interfacing with living cells using advanced micro-/nano-electronics enables new sensing and actuation
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innovative methods to leverage machine learning for numerical weather forecasting and climate modeling. Project background We are looking for a motivated Machine Learning Scientist to join the development team
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Structuring and follow-up of project ideas Execution and coordination pre-studies Preparation of industry and grant proposals Development of collaboration opportunities with other research groups Development
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software development of their research. The position focuses on developing Python and C# libraries for research in architecture, civil engineering and extended reality (XR), building on the open-source
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affect technology adoption, industrial development, policy design, and its socio-technical and political feedback effects. The project is embedded within ETH Zurich’s new Einstein School of Public Policy
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. Empa is a research institution of the ETH Domain. The Urban Energy Systems Laboratory (UESL) pioneers strategies, solutions, and methods to support the development of sustainable, resilient, and
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manufacturing and operations to support development, integration, test, launch, and on-orbit commissioning Provide on-orbit or mission support, including anomaly resolution and telemetry evaluation
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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive