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the world’s most challenging problems through artificial intelligence and data driven algorithms and systems. CSMD creates the mathematics, artificial intelligence, and architecture-aware algorithms
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 2 months ago
Conference on High Performance Computing, Data, and Analytics (HiPC 2017). https://legion.stanford.edu/pdfs/hipc2017.pdf Visibility Algorithms for Dynamic Dependence Analysis and Distributed Coherence. Michael
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Location ESRIN, Frascati, Italy Description Data Quality and Cal/Val Manager for Atmospheric Sensor Missions in the Sensor Performance, Products and Algorithms Section, Earth Observation Mission
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networks, online analysis with delay, and theory of distributed algorithms. Job description In our group, we try to apply and unite the approaches and techniques of theory and practice. Some members of our
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-board Payload Signal and Data Processing algorithms and techniques for RF payloads and instruments in close collaboration with TEC-ED; and Time and frequency references, modelling, design tools
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of distributed computing, machine learning, image and text analysis, randomized data structures, high-performance computing, and quantum algorithms. Beyond this research, we aim to support computational thinking
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for improving the simulation and optimization of distributed systems, for instance by specializing neural ODEs and their training routines. This research will address challenges driven by the energy transition
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processing and analysis. The LISA ground segment is a distributed facility (the LISA Distributed Data Processing Center), for which LISA member states contribute national centers with specific commitments and
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the development of data processing scripts and pipelines to the development, distribution, and maintenance of free software for the analysis of genetic data. This is an opportunity to work in a challenging and
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comparing models with entirely different structures and parameter counts, whether comparing linear regression against mixture models or decision trees. MML is strictly Bayesian, requiring prior distributions