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assimilation, and at least a practical understanding of machine learning. Both profiles should bring a curiosity for bridging disciplines and a drive to innovate at the intersection of AI and ocean science
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processing, visualization. You will explore new avenues in coherent imaging, e.g. exploiting machine learning or introducing new techniques exploiting the EBS-enhanced coherent photon flux. You will also
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machine learning to model network behavior from real-world measurements (e.g., [7]). Although promising, these approaches still face three major limitations: (i) they often rely on idealized and extensive
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. 5, no. 2, pp. 354–379, 2012. [2] C. K. Williams and C. E. Rasmussen, Gaussian processes for machine learning. MIT press Cambridge, MA, 2006, vol. 2, no. 3. [3] G. Daras, H. Chung, C.-H. Lai, Y
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existing SC analysis tool, by integrating machine learning and benchmarking components, thus helping evolve it into a market-ready solution capable of real-time threat intelligence and adaptive vulnerability
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, https://hal.science/hal-04930868 . [2] Peyré, G., Cuturi, M., et al. (2019). Computational optimal transport: With applications to data science. Foundations and Trends in Machine Learning, 11(5-6):355–607
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Automated Generation of Digital Twins of Fractured Tibial Plateaus for Personalized Surgical plannin
in Computer Vision; 2009 Oct 12–16; Trégastel, France.Available from: https://inria.hal.science/inria-00404638v1/document 5. Micicoi G, Grasso F, Kley K, Favreau H, Khakha R, Ehlinger M, et al
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The candidate should preferably have a PhD in Computer Science or Robotics with a solid background on deep learning and 3D scene understanding. Experience with LiDAR and Computer Vision is a plus. The candidate