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to its activities, see https://csd.ens.psl.eu The Laplace chair aims at recruiting outstanding candidates in all areas of data sciences including theoretical statistics, machine learning, deep learning
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interest: Advanced techniques for data storage/retrieval/processing/visualization on large scale. Cybersecurity Software engineering Machine/deep learning Technical aspects of human computer interaction (HRI
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information extraction from images or videos, object detection and tracking techniques. - Machine learning and artificial intelligence: mastery of supervised and unsupervised methods (CNN, clustering
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, CNRS, I3S, Sophia-Antipolis, France) Collaboration: Luca Calatroni (Luca.calatroni@unige.it), Machine learning Genoa Center, Italy. Context and Post-doc objectives Conventional optical microscopy
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properties changes. - The demonstration of the tear detection with machine learning classification applied directly on S-parameters of the MWI system without solving the inverse problem. The objective
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machine learning. We particularly value depth of knowledge, originality, and the potential for cross-disciplinary innovation. Relevant application areas may include (but are not limited to) natural
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simulation tools and machine learning techniques. Initially, the work will be based on the design of a database of medical images, which will be processed using convolutional neural networks to identify design
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techniques for data storage/retrieval/processing/visualization on large scale. Cybersecurity Software engineering Machine/deep learning Technical aspects of human computer interaction (HRI, multi-modal
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range of computer vision tasks. Their strong representational capacity, however, comes at the price of significantly higher computational complexity and memory requirements. This poses a major challenge