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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 18 days ago
of the Cluster IA ENACT project (https://cluster-ia-enact.ai/ ) that is funding this PhD thesis. In the chair, she wants to push the research in Natural Language to assist humans in different scenarios
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of a sustainable industry. At the same time, complexity is increasing through digital decision support systems, sensors, AI, and augmented reality. The goal is to develop knowledge and concrete design
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or replacement, and removes defective equipment. Exposes & scans digital radiographs using digital sensor or phosphor plate. Assists with maintaining established departmental policies and procedures, objectives
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Exploration - Source localization 2. Perception in sensor-degraded environments: - Localization in smoke and dust filled environments - Scene awareness - Biometric/triage evaluations, etc. 3. Mission-specific
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of microelectronics, quantum sensors and devices, and sustainability sciences. Whether it be through science, engineering, technology or professional services, every team member has an opportunity to contribute to our
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with experimental techniques including current meters, pressure sensors, and GPS surveying. Proficiency in computational methods include large-scale and high resolution hydrodynamic models. Experience
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techniques for analyzing data collected from hardware sensors installed on electrospindles and the development of models for calculating the spindle’s remaining useful life. The contractor will explore topics
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from observations, sensors, or eDNA, in combination with environmental drivers, land-use history, and management. Experience with large and heterogeneous datasets is important. The successful candidate
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portable dataloggers (or analogous geophysical sensors) and continuous monitoring workflows. Background in UAS/drone mapping. Numerical modeling skills. Familiarity with coastal water-level/wave datasets and
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., food processing, bioconversion and fermentation processes) Self-supervised learning for multimodal sensor data (e.g., spectroscopy, hyperspectral imaging, 3D point clouds, and other high-dimensional data