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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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models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to rupture, which is one cause of a stroke and thus the prediction of plaque rupture is very
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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | 3 months ago
Automata (CAFE) to considerably reduce calculation times and improve the response of numerical models. The numerical models will include laser-matter interaction, melt, and microstructure prediction models
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will focus on designing computationally efficient, scalable, and adaptive AI models that operate under strict constraints in radio access, edge, and non-terrestrial network environments. The position is
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9 Feb 2026 Job Information Organisation/Company Université Gustave Eiffel Research Field Computer science » Modelling tools Engineering » Electrical engineering Researcher Profile First Stage
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-package of the ChiExCo program, which aims to develop a reliable computational protocol to predict, for organic chromophores, both chirality quantifying factors (gabs and glum) resulting from excitonic
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into the wave interaction and propagation processes. Modelling the propagation characteristics of optical communication systems with a focus on optical atmospheric turbulence and statistical prediction models
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- specific predictive models, the lack of explainability in AI-driven decision processes, and the difficulty of capturing long-term dependencies in time-series data. In this project, you will focus
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the complex multiscale nonlinear interactions at the origin of such extreme events. In this project, you will develop machine learning-based reduced-order models which can accurately forecast
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interventions can promote cognitive abilities in aging. The main task is to develop methods for predicting health outcomes using dynamic and adaptive modeling whilst addressing computational challenges