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the emergence of edge computing, data storage will become more geo-distributed to account for performance or regulatory constraints. One challenge is to maintain an up-to-date view of available content in such a
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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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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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PhD degree in Computer Science, Physics or a related field Experience with parallel programming models Strong programming skills in C/C++ and/or Python Knowledge of distributed memory programming with
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arrival. By joining our team, you'll have the opportunity to contribute to the future of real-time verification, making a meaningful difference in the world of distributed systems. If you're passionate
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Virtual laboratory to predict the ability of a fluctuating biomass to satisfy a material use-VARIOUS
and organic waste) of which the properties are different and subject to seasonal and climatic variations. For the bioeconomy, it is therefore necessary to sort biomass sources according to the target
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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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Description This PhD project bridges computational neuroscience and machine learning to study the mechanisms of active forgetting—or unlearning—through the lens of both biological and artificial systems
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of supercomputing libraries for supercomputers. The position is located at Maison de la Simulation team (https://mdls.fr), in Saclay (near Paris), but our team is distributed in the following other locations: Inria