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. Indeed, the methods currently used rely on optical image databases of various avalanche observations. A deep neural network was trained on this data to enable automatic avalanche detection FIGURE 1 (a) [1
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 12 days ago
to reconstruct open rose flowers in 3D. The key idea is to learn two neural networks that operate on different scales. The first network operates on the scale of the full flower to identify the flower architecture
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the direct incorporation of physical laws or constraints into the neural network training process. To achieve our goal, we will conduct analysis of several surface water management scenarios in different
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evaluation of advanced applications for network benchmarking, road hazard warning, and pseudo-remote driving supported by computer vision, while also supporting the broader research and integration activities
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these degradation pathways. Join our team! Decoding Light-Induced Degradation through Molecular Networking and Data Science Many organic compounds change under the influence of (UV) light. This is beneficial in water
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approach makes it easier to identify different local optima using sampling mechanisms. In stochastic optimization, distribution estimation algorithms (EDA) are an alternative approach to traditional
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position on vulnerability and adaptation to climate change inside the Mediterranean Ramsar network. The Tour du Valat is a research institute for the conservation of Mediterranean wetlands based in
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). At all scales, faults form dense networks (aka fault zones) including a master fault and myriads of secondary fractures and faults that intensely dissect the host rock. Whether it is a master or a
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of the project, during the generalization of the territorial diagnostic tool and the exploration of scenarios in different geographical contexts. Mission The recruited researcher will provide a comparison of
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for their actions and possibilities in the host country. The Urban Development and Mobility department brings together researchers from a number of different disciplines (including geography, economics, sociology