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Python and a profound knowledge of state-of-the-art statistical methods for climate data analysis Working experience with climate (model) data, in particular with large climate model ensembles, and
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might affect many services throughout a network by means of propagation. The project focuses on timetable planning that is able to minimize delays and their impacts. To do so, large-scale optimisation
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Federal Institute of Technology - ETH Zurich, on Multi-modal SAR for large scale rapid change observations in permafrost regions. Job description The research focuses on investigating/developing generic
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- focused on geospatial AI, Earth observation foundation models and the analysis of large data volumes - and with external partners to drive pilots, field validation, operational testing and integration
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need behind an idea, to work as a team, to think big, to fail, to pivot, and to keep overcoming obstacles they encounter on the way. Our first location was established at ETH Zurich. As we scale and
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this research are fundamental and timely: How can we ensure AI-discovered quantum algorithms are not classical in disguise? How can we generate large-scale, high-quality quantum training data? And how can we
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and analyses of big data amounts - and external performers to drive field pilots, validation, operational testing and integration into agricultural workflows Identify and mobilise strong research groups
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promoting the resilience of supply chains by building and solving quantitative global equilibrium models of trade networks. We will use firm-level data to estimate the industry/country-level effects
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comfort automating infrastructure (Ansible, Terraform, or similar). A working understanding of how ML and scientific workloads consume data — billions of small files, large checkpoints, sharding — and why
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journey, from the collection and management of data to machine learning, AI, and industrialization. With a large multidisciplinary team of professionals across three locations (Lausanne, Zurich, Villigen