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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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of cryptographic algorithms through solving polynomial systems of equations. It is crucial for building confidence in quantum safe cryptography, as well as novel symmetric encryption algorithms designed for use with
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the Department). About the project/work tasks Algebraic cryptanalysis examines the security of cryptographic algorithms through solving polynomial systems of equations. It is crucial for building confidence in
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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML
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of Informatics and is hosted jointly by the Network and Distributed Systems Research Group and the Robotics and Intelligent Systems Research Group. The research groups consist of around 30 full- and part-time