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. You'll work at the exciting intersection of experimental materials science and materials informatics, collaborating with CSEM and EPFL in a Swiss National Science Foundation (SNF) Bridge Project. Your
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scientific questions or develop algorithms that perform automatically some challenging tasks. This typically involves exchanging actively with collaborators and domain experts to understand the precise
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strategic focus area of the ETH domain, with EPFL and ETH Zurich as founding partners. Its mandate is to support academic groups and research, hospitals, industry, and the public sector at large, including
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systems. Your work will help to define the quality and features of our algorithms. Armed with your innovative spirit and project experience, you will manifest fresh ideas and novel approaches
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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
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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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utilization, adapt or modify algorithms to run more efficiently and consult, train and support our users on best practices along those directions. You are expected to take a holistic approach considering
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: graph neural networks, natural language processing, algorithmic learning, fault-tolerance, blockchains, consensus, cryptocurrencies, digital money, central bank digital currency, decentralized finance
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, and integrated analysis. The team runs an excellent open source based software environment and establishes state-of-the-art data analysis concepts and algorithms. Job description The bioinformatician
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