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data-silos, like hospitals that cannot share their patients' data [4]. Research goal: One of the main scientific challenges of FL, in comparison to other forms of distributed learning, is statistical
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the ability of neural networks to learn unknown posterior distributions distributions. Their use in the field of image microscopy, however, remains limited. The purpose of this PhD thesis is to develop
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to obtain funding for PhD students. In parallel, applications to FRM and/or Pasteur-MD-PhD-PPU program will be also encouraged. Opportunities for Interdisciplinary Training: Depending on the candidate’s
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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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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
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will be considered a plus: Data analysis libraries such as Dask Knowledge and experience with Python, Fortran and/or GPU computing HPC and parallel libraries such as OpenMP and MPI HPC parallel IO
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Virtual laboratory to predict the ability of a fluctuating biomass to satisfy a material use-VARIOUS
be taught within the various courses at the ECN and NU with a 50/50 distribution between the engineering (ECN and NU Polytech) and Master’s 2 courses. Concerning the ECN, it would be desirable
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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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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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SPINTEC fabrication facilities. As such, this thesis will be stationed mostly at SPINTEC in Grenoble, the PhD program being registered at Université Grenoble Alpes. You will also be part of the Vertical