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rates. This PhD project focuses on developing a mathematical model to predict and analyze the effects of using a gene-drive strategy to control Schistosoma mansoni directly, rather than through its snail
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sciences, particularly in the domain of numerical weather prediction. On the one hand, state-of-the-art models such as GraphCast have demonstrated outstanding predictive skill. On the other hand, physics
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bistability. Despite their fundamental role in biology, these membrane properties are difficult to predict from in vitro membrane systems or in situ fixed samples. This challenge arises because biological
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is to provide the most **informative** samples (not just the most probable) to facilitate identification and enhance user experience. - **Theoretical Model Improvement:** Understanding a sample's
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beetle as a model system, the candidate will test the hypothesis that sexual selection in females promotes the evolution of female reproductive traits. In addition, the project will take a broader
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of electrical topologies (AC, DC, three-phase) and will integrate both standard and atypical wear cases. On this basis, high-performance artificial intelligence models will be developed. By combining neural
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of Communities team and interact with its members. The modeling work will also involve collaborations with researchers from CEFE (Montpellier), BIOGECO (Bordeaux), and forest management partners (ONF). Our little
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to modern genomic datasets that may involve hundreds of populations. He/she will also develop probabilistic GO models inspired from the Redundancy Analysis approach and extend it by introducing Neural
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, engineering, bioinformatics, machine learning, artificial intelligence) to support minimally invasive and targeted preventive and predictive medicine capable of limiting age-related functional disorders
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environment featuring a wide range of research activities, including QM/MM simulations, ionic liquid simulations, and excited-state characterization. The aim of this PhD thesis is the atomistic modeling