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objective is to develop artificial intelligence methods that complement modeling techniques, particularly at the atomic scale, specific to the physical chemistry of materials. In this context
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to (i) combine the halogen-bonding (XB) properties of HAs with complementary binding motifs to construct more efficient and selective receptors, and (ii) enhance their stability by protecting them from
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The METHODS team of Centre for Research Epidemiology and Statistics (CRESS) is seeking a postdoctoral researcher in epidemiology to support the scientific implementation of the "CPG-EMULATE" project (Level of
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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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Inria, the French national research institute for the digital sciences | Bures sur Yvette, le de France | France | 5 days ago
theory, and data-driven methods. The postdoc will work in close collaboration with an interdisciplinary group of researchers and students in control theory, signal processing, neuroscience, and sports
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, opportunities and effectiveness of methods for quantifying the environmental impacts of AI The following activities are expected: - conduct between 10 and 20 semi-structured interviews on the ecosystem of French
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perception with a comparative, cross-species (animal – human) component. The selected candidate will establish a non-human primate (NHP) model of scalp EEG signals related to perception, compare it to human
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elements of the chain (transmitter, data centre, core and access network, terminal). This model will be based on a combination of data from the literature, life cycle analysis databases and assessments
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them a key component of these ecosystems. Used since pre-Columbian times for food, fibers, and medicinal purposes, they are both culturally important and economically valuable, and serve as biological
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represented by them in the context of the microbiome. Here, using a triptych approach leveraging in silico, in vitro and in vivo methods, we aim to specifically and systematically characterize and validate