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Field
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SyMulDaM project involving the development of predictive models to quantify the integrity and durability of a nuclear power plant containment structure., within the mechanical engineering department
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computational physics and/or geosciences and a strong interest in machine learning, Earth observation, and numerical modelling for Arctic prediction. The PhD project will develop methods to improve monitoring and
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from semantic radio maps; learn which features act as reliable predictors of rewards or outcomes; associate these features with predictive models that guide decision-making; exploit such cue–outcome
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AI researchers from ANITI, IMT and CERFACS, as well as with researchers/engineers in weather forecastings from the CNRM (Météo-France). Hybridization methods between neural networks and physical models
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, better adapted individuals can be selected at the seedling stage using only genetic data, accelerating the breeding cycle. Incorporating information about plasticity can aid genomic prediction modeling
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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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on the vision of developing multi-level thrombosis risk prediction models, from cellular dynamics to organ-level hemodynamics. The network integratesin silico, in vitro, and in vivo approaches to understand
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the conditions of crop, pasture, and their environment with advanced remote sensing and geospatial technologies; Develops and refines algorithms and workflows for crop and pasture monitoring, modeling, prediction
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for anisotropic laminae and laminates (e.g., layer-wise / higher-order plate models) to accurately predict stress fields and assess cloaking performance. Build a staggered multi-scale simulation workflow (from
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, that combines diffusion and transformer models, there are clear indications that the analysis of this data can be automated. This will open new avenues in data interpretation and building predictive models