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the implementation of mechanical testing combined with a variety of sensors to follow changes in the structures. You will use a range of surface analytical techniques to characterize the detected changes. You will
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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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: 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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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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proven experience in FPGA programming and high-speed electronics circuit design and testing. Experience in quantum information processing and quantum communication algorithms, as well as fiber optics
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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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) or neural network-based methods. The level of the targeted problems will require further mathematical and algorithmic developments over the current state of data-driven SSM reduction. The PhD position will