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statistics and machine learning, focused on identifying abrupt shifts in the properties of data over time. These shifts, known as change-points, indicate transitions in the underlying distribution or dynamics
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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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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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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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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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Excellence Programme , which prioritizes the recruitment of female scholars to professorial roles. For an initial period of six months, the University will consider applications exclusively from female
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group: MARIANNE (https://team.inria.fr/marianne/). The MARIANNE project-team pursues high-impact research in Artificial Intelligence with a focus on data and models for computational argumentation in