139 evolution-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"ISCTE-IUL" positions at ETH Zurich
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as well as stable isotopes in tree rings. Forest sites are located in Switzerland and Czech Republic. Job description Your main tasks will include: Development of knowledge-guided machine learning
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of grid-scale energy storage technologies, but investigate the topic from different perspectives, including technological development, reliability and security, integration and coupling, socioeconomic and
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to efficiently train on the large, densely-connected and graph-structured data encountered in our systems of interest. Your contributions would be across the spectrum from methodological development
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experimental and computational researchers on study design, data interpretation, and the development of reproducible computational workflows Support research data management, including data organisation, storage
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contributions to the Swiss AI Initiative and similar programs, such as lending support for the development and release of the Apertus models. The initial two-year contract could potentially be extended or even
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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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to the development of new theoretical and computational methods for the analysis and design of biological dynamical systems. The positions are funded by a European Research Council (ERC) Advanced Grant. The successful
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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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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