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. Scientific IT Services (SIS - a section with ITS) aims at bridging the gap between computational research and IT services and infrastructure provisioning. We are working closely together with ETH researchers
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. Integrate various datasets, such as tree species annotations, climate, and topography, into deep learning algorithms. Test deep learning models (Transformers and CNNs) for optimal accuracy using large
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technologies promise to revolutionize multiple branches of science by solving problems that cannot be tackled by classical systems. While efficient and large-scale quantum computers are still far from being
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natural language processing to algorithmically detect hate speech across a variety of online venues (newspaper and social media). To do so, we need an up-to-date, high-quality corpus of training data. Job
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institutions, to build a framework for discovering, sharing and executing data and algorithms in a distributed environment. The main technology will be Python, though Scala and Typescript knowledge are a plus
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expertise? Through an Impact Collaboration Programme (ICP) project, you can develop creative ways to work together to ensure that the best available knowledge supports the design of solutions to the complex
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DIZH understands innovation very broadly and includes all disciplines: artistic, design, natural science, technology, humanities, education and social science.
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using learning-based algorithms Development of a simulation framework for stressor analysis and traffic equilibrium modeling Integration of predictive analytics and multi-agent reinforcement learning
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algorithms and AI-based solutions for data processing and validation, and provides scientific expertise for the implementation of future remote sensing missions. The team’s work bridges earth observation with
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to muon g-2 from lattice Quantum Chromodynamics and algorithmic developments for multi-level and RG-improved simulations (research group of Urs Wenger) C.) Study of multi-hadron systems, with a focus on