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, the ability to analyze the full dataset collected by the experiment will be severely limited. The L2IT is a leader in developing new track reconstruction algorithms using geometric deep learning methods
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, ocean acidification, shell quantity and organic-matter content on dissolution. • Characterise the microbial communities associated with the shells (16S rRNA sequencing, exposure to deep-sea bacterial
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) and/or machine learning (about 10 PIs). The Physics Laboratory is about 180-member strong and conducts world-leading research on a broad range of topics, including quantum technology, statistical
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language processing that address concrete problems and are both theoretically rigorous and interpretable. The PhD is funded by the ERC CoG PANDORA (Deep Multimodal Learning for Mining and Generation of Arguments
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, visiting researchers, master's students, etc.) Research Context Recent advances in mobile robotics have been driven by remarkable progress in perception, deep learning, and control. However, current robotic
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. • Karimi H. et al., “Wavelet Based Protection of Microgrids”, IEEE Transactions on Smart Grid, 2019. • Heidari A. et al., “Deep Learning for Fault Detection in Smart Grids”, Applied Energy, 2021. • Wen L. et
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-induced wounds. At the molecular level, frequent exposure to acute or chronic tissue damage in epithelial tissues has recently been shown to activate plasticity mechanisms, with a deep impact on the cells
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representation of the vertical coordinate in numerical ocean models used to simulate ocean currents, temperature, and salinity. These developments will improve the representation of deep-ocean circulation and
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generated by cleared tissues. Imaging deep within biological tissues remains challenging because of optical aberrations caused by spatial variations in the refractive index. These aberrations degrade image
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-house laser training and on-the-job training. Optically active deep defects in the solid state (or color centers) can be seen as artificial atoms. They represent a major interest in quantum