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implement and train neural network architectures, including Physics-Informed Neural Networks (PINNs), in order to integrate physical constraints into the learning process and improve the identification and
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missions: - Characterization of liver somatic mosaicism. - Identification of novel genes using ultra-deep WES. - Analysis of CHIP prevalence and impact in chronic liver diseases. - Integration of genomic
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networks (GNNs) to accelerate therapeutic target identification. GenePPS aims to overcome current limitations of perturbation modelling by integrating large-scale single-cell foundation models with
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-rich environments. The candidate will participate in field sampling, species identification, and laboratory cultivation of nematodes from extreme habitats. The candidate will also take part in molecular
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-modal models combining clinical, genomic, and immune analyses. - Participate in predictive modeling of oncological outcomes and treatment response. - Contribute to the identification of novel therapeutic
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porous solids for the capture and/or degradation of toxic agents (or simulants) and sensors. Main activities Identification of MOFS composition Using existing databases that have already identified
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, DSC, TGA, FTIR, mechanical and rheological properties, etc.) - Development of waterborne polyurethane dispersions (PUDs). - Identification of the best formulations meeting specifications
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matter gravity simulators, enabling the identification of exotic properties of relativistic materials. Use of various theoretical methods to study the effects of field theory properties in curved spacetime
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effects on the performance of an integrated circuit (IC). 2. Identification of aging and/or performance signatures that can be extracted using non-invasive and non-destructive methods, such as imaging
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all necessary analytical tools for the identification and quantification of reaction products. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7285-CAMABR-002/Candidater.aspx