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: - Apply unsupervised machine learning concepts to the analysis of continuous seismograms recorded in the vicinity of active volcanoes, in order to extract information about the state of the volcano and the
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advances obtained on Graph Neural Networks, on both convolution [5] and pooling [6] steps. 1.2 METHODOLOGY AND WORK PLAN The proposed PhD research program aims to develop innovative graph machine learning
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Infrastructure? No Offer Description Project title: Functional reading of moving texts applied to the ergonomics of digital learning tools. Subject: As part of the SciCoNum project, and in partnership with
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in the field of medical imaging. The team particularly studies the potential of machine learning methods for an efficient and relevant representation of medical data such as images. The challenges
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controlling these molecular networks in vivo . This will be achieved using scRNAseq, spatial transcriptomics, advanced machine learning approaches, and genetic approaches to manipulate the expression of
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after learning will help to map the brain networks of memory. The PhD project will be subdivided into 3 consecutive parts: 1) Mapping the brain regions activated during memory formation in a simple
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unprecedented development of THz spintronics. However, these works are mostly focused on emission and there is a clear need for efficient scheme for THz detection. In this PhD project, we propose to use
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evolvability. Funding category: Contrat doctoral PHD Country: France Requirements Specific Requirements Eligibility criteria Specific Eligibility Criteria of the Horizon Europe Marie Skłodowska-Curie (MSCA
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expertise in macromolecular interactions and docking, structural biology, and deep learning, together with several PhD and master students. Assignment Background: Streptococcus Pyogenes (Group A Streptococcus
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Description of the offer : PhD scholarship to work on a novel sensing approach exploring spin transfer torque (STT) for magnetic field sensing in perpendicular anisotropy magnetic tunnel junctions