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Field
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of: electromagnetic sensor design, modelling, prototyping and experimental validation. The best fit is A candidate interested in high-frequency measurement techniques, sensor hardware development and precision
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-fidelity modeling approaches combining simulations of different fidelity levels, ranging from RANS calculations to high-fidelity LES, possibly associated with various levels of geometric or physical
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material models, the development of Digital Twins, and the use of AI-based methods. This includes building model-ready databases, developing simulation chains that link process simulations with analysis
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
) solvers are a key element in simulating dynamics, but their execution times, even on supercomputers, make them difficult to deploy for digital twins subject to strong reactivity constraints. Meta-models
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Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field. Demonstrated experience developing AI and machine learning models for biomedical applications. Job
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(materials, scenarios) - Translation of local knowledge into simulation rules - Facilitation or co-facilitation of model testing sessions - Collection of feedback for prototype iterations. Analysis and
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of interest, e.g. the spectral density of strongly correlated models, as measured in ARPES. • The mathematics of the approach will require a lot of innovative developments too. In the only published article
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offers the opportunity to contribute to an emerging area of interdisciplinary research that brings together artificial intelligence and advanced materials modelling to address key challenges in
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. The successful candidate will combine extensive experimental testing with insulation lifetime modelling to study PD inception, degradation and endurance, and to develop approaches for predicting insulation
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be a game changer. Deep learning models can learn the mapping between material states and ultrasonic responses from simulation data, delivering quantitative predictions once trained, and remarkably