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antigens presented by MHC molecules. This curiosity-driven project develops cutting-edge AI technology for 3D TCR–pMHC modeling to improve neoantigen identification, TCR specificity prediction, and TCR
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models can predict the performance of marketing content before it is published. Research at the Center builds on the group's track record in data science, conversational AI, and marketing analytics and on
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planetary atmospheres. The Section also initiates and manages a wide range of related modelling, software and hardware R&D activities. You are encouraged to visit the ESA website: https://www.esa.int/ Field(s
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Potentials (MLIP), machine learning (ML) predictive models and AI tools. Activities : Computer science implying ML and AI tools applied to material science Where to apply Website https://umontpellier.nous
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Engineering of Catalysts for Hydrofunctionalization Reactions: From Selectivity Control to a Predictive Model” project financed from the funds of Priority 2 of the European Funds for a Smart Economy Program
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models to explain differences between theoretical predictions and experimental observations. Work closely with other researchers in the group to integrate new experimental methods into hyperpolarised xenon
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, interactomics, structural biology, and imaging datasets into predictive computational frameworks. Application of atomistic simulations, coarse-grained modeling, RNA folding prediction, RNA-protein and RNA-small
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solutions for offshore wind turbines, enabling to enhance their structural awareness, real-time reliability assessment, and predictive maintenance decision support through integrated sensing, modelling, and
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to the company, using AI agents; and (iii) develop reliable predictive models regarding healthcare access strain and epidemic risks, while ensuring a high level of personal data protection. Today, most companies
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observations/models and from models of social dynamics. Motivating aims include the discovery and analysis of hidden large-scale patterns in data/models, elucidating mechanisms underlying the emergence