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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
machine learning, 3D visualisation and real-time images (augmented reality). The main robot will the Tiago++, by PAL Robotics, which is a modern omnidirectlonal, bimanual robot. With the help of a PhD
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imaging and machine learning. The main task of the successful candidate will be to help redefine certain traditional criteria of comparative anatomy used in archaeozoology and to establish new criteria
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). Require skills Strong background in communication networks and wireless/satellite systems; Knowledge of machine learning and optimization techniques; Experience with simulation tools (Python, MATLAB, ns-3
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
-PULSE addresses a key open question in responsible AI: can we design practical machine learning systems that satisfy strong privacy guarantees [1] and fairness [2] constraints simultaneously, without
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of the ERC Consolidator project AUTOMATIX (see details below), we are seeking a PhD candidate to develop machine learning approaches for constitutive modeling. Context With the advent of machine-learning (ML
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been revolutionized in recent years by machine learned interatomic potentials (MLIP), and questions that were impossible to tackle five years ago can now be addressed. The state-of-the-art approach
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models will then be developed by integrating radiomic features with clinical variables, including the KELIM score, mutational status (BRCA/HRD), and pathological parameters. Machine learning methods
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Sophia Antipolis, Provence Alpes Cote d Azur | France | about 2 months ago
problèmes complexes. -Bonnes compétences en gestion du temps et en communication.- Background in probability, statistics, machine learning, and wireless communications. - Interest in causal inference
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mechanics, finite element modeling, and scientific machine learning. The RSE will contribute to the design, implementation, and maintenance of open-source software libraries that integrate phenomenological
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analyses of declustered catalogs will quantify background seismicity changes, a sensitive proxy for stress evolution on the fault. Machine learning approaches will be used to characterize empirical