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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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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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the exact calculation of the square-root and inverse square-root of the source distribution covariance matrix. This approach offers analytical and computational advantages in comparison to existing methods
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programmes; and teaching and learning initiatives Secretary to the Faculty's Teaching and Learning Committee (TLC) and coordination of the implementation of the decisions of the TLC Support the Faculty and
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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fields for several applications in the field of computer vision and inverse problem [SLX+21]. As far as the modeling of data term between distributions is concerned, one idea would be also to follow
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Supervisors: Laure Blanc-Féraud (blancf@i3s.unice.fr), Xavier Descombes (xavier.descombes@inria.fr), Sebastien Schaub (sebastien.schaub@imev-mer.fr) Host institution: MORPHEME research group (INRIA, CNRS, I3S, Sophia-Antipolis, France) Collaboration: Luca Calatroni (Luca.calatroni@unige.it),...
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advances in machine learning and data-intensive approaches facilitate the search for better or even global minima via evolutionary computations or reinforcement learning. Objectives. The main scientific