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algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric
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DTU Physics seeks a talented and motivated postdoctoral researcher for a 2-year position in computational nanophotonics. The goal is to use inverse design, such as topology optimization
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computational models, algorithms, or theory to probe the mechanisms of learning and memory. Ideal candidates will work across levels—from molecules and synapses to circuits and systems—and contribute
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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at the interface of machine learning and computational neuroscience. The candidate will be part of the COATI joint team between INRIA d’Université Côte d’Azur and the I3S Laboratory. Project The candidate should
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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active research interests in quantum computing, quantum algorithm/software development and applications in decarbonisation who can take full advantage of the unique opportunities the QDA provides
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for functional group. Work on complex algorithms and coding to integrate all SDTM specifications and database specifications. Build governance database to create a repository of all governance requests
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) models. Little attention has been paid to other models like graph NNs (GNNs) or PCA. Among the few existing works in the literature, [2] proposes an FL algorithm to compute PCA in a DP fashion, but the
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revisit discretization methodologies in view of modern requirements and computational capabilities. The candidate will focus on developing mesh generation algorithms meeting the following criteria