183 evolution-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S"-"U.S"-"St" positions in Switzerland
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opportunities for professional and personal development. Start date is May 2026 or upon agreement for a duration of 2 years. We live a culture of inclusion and respect. We welcome all people who are interested in
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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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development, but also actively contributes to positive change in society The planned duration of the initial contract is one year, to be extended based on continued funding and successful performance
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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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; Astronomical Instrumentation; Optical Design Skills Development During the PhD, the student will develop expertise in: Extreme-precision radial velocity (EPRV) instrumentation Optical design and tolerance
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Observations; Stellar Variability; Time-Series Analysis Skills Development During the PhD, the student will develop expertise in: Ground-based exoplanet transit surveys Operation, installation, and commissioning
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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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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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the structure and function of plant pathogen genomes. We are looking for someone with prior experience in either fungal or transposon comparative genomics, who can explore the diversity and evolution of Starship
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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