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channels, transporters, and stress signaling systems. The project requires thorough knowledge of bacterial cell biology and molecular genetics as well as hands-on experience with cloning, live-cell
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and molecular genetics as well as hands-on experience with cloning, live-cell fluorescence microscopy, image analysis, and sample preparation for sequencing and multi-omics analyses. The main model
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of infancy, primary immunodeficiencies are now viewed as more common, also explaining disease in children, adolescents, and adults. With high-throughput genetics, rare, potentially disease-causing variants can
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and tomographic radar capabilities. Our team is responsible for the algorithms which derive the biomass data product. The post-doc project is about extending the biomass algorithm to also include data
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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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expertise in existing methods and state-of-the-art in the field. The position includes algorithm design, software implementation, and validation on experimental datasets. You will contribute to building a
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application! Work assignments This position focuses on the development of theoretically grounded and practically scalable decentralized learning algorithms under realistic system constraints, including
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existing methods and state-of-the-art in the field. The position includes algorithm design, software implementation, and validation on experimental datasets. You will contribute to building a flexible
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Are you interested in developing machine learning algorithms that provably help us make better decisions? Join us as a post-doc in the Division of Data Science and AI, Department of Computer Science
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, experience working with the PyTorch framework, documented ability to develop algorithms and implement them in efficient code, and experience in statistical modeling, optimization or numerical methods, as