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unit (UMR 7248) from UCA and CNRS. Abstract Optical flow estimation is a key task in computer vision, particularly critical for autonomous navigation where accurate motion perception is essential. It can
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, so that it can be easily used in practice (fast optimization, embedded decision-making, online updating). 1. Design a lightweight statistical/probabilistic surrogate model, integrating: • an estimation
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on fluctuation of fluorescent molecules methods as they don’t need any specific materiel or fluorophore. The super-resolved image reconstruction is formalized in mathematical terms as an inverse problem with
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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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experimental protocols and numerical models, encompassing steady-state analyses and advanced techniques such as Laplace and Fourier transforms. Furthermore, inverse modeling methods were implemented to estimate
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Approximate Curvature) are employed. - **Extensions:** Zero-order optimization and natural gradient methods are explored to address computational issues and implicit bias in optimization algorithms
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characterized by a vast number of underground shallows abandoned mines due to its geological features and industrial legacy. It is estimated that there are hundreds of thousands of these mines, many of which
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on estimated movements using eDCCs. The research will focus on data simulated using the Monte Carlo method and real data from clinical SPECT scanners with a parallel collimator, such as those available at LUMEN
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these active modules in complex networks have been proposed [1] and are being studied by the SPARKS team at I3S [2-3]. The mission of the recruited researcher will be to develop a method for identifying active