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projects, including the flagship EXCELSIOR H2020 Teaming Project (https://excelsior2020.eu/ ), as well as DIGIFARM, NOSTRADAMUS, CERBERUS, and CARBONICA. ERATOSTHENES Centre of Excellence is an autonomous
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algorithms for the evolution of inorganic aerosols in the atmosphere, building upon the research group's efforts in modeling combustion-generated aerosols. These modeling framework will be used to understand
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to support a two-year Small Business Technology Transfer project (STTR) funded by the National Science Foundation. The successful candidate is expected to support the development, simulation and
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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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, such as new topological states, magnetism, electrical/thermal transport, superconductivity, and corresponding material predictions. (2) Develop numerical algorithms and computing software, such as
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to contribute to cutting-edge research in robot intelligence, machine learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms
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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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-based decision-support platforms for real-world transportation agencies. The Postdoctoral Associate will join a multidisciplinary team of faculty, researchers, software developers, and agency partners
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scientific breakthrough and to contribute to the welfare of society and humanity through developing innovative technologies. We also conduct research on ethical, legal and social issues caused by the spread
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 3 months ago
accurately study the selected algorithms, participate in the development and maintenance of the PEPit (https://pepit.readthedocs.io/ ) and AutoLyap (https://github.com/AutoLyap/AutoLyap/ ) software packages