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experimental data (from ex-situ and in-situ measurement). Therefore, she/he will develop a way to optimize/guide the experiments trough artificial intelligence approach (machine/deep learning) that he will
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
Framework Programme? Not funded by a EU programme Reference Number 2025-09322 Is the Job related to staff position within a Research Infrastructure? No Offer Description Context. This PhD thesis is part of
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: electronic structure calculations (plane wave DFT if possible), statistical thermodynamics, molecular dynamics. Skills in Python, bash scripting, Fortran 90 and machine-learning would be appreciated. The PIIM
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technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in
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-flexible technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their
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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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machine learning with the logical reasoning and semantic understanding of symbolic AI (often referred to as material and design informatics) is being developed for the accelerated discovery and development
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 2 months ago
Emeraude INRIA/INSA-Lyon research team which is physically based at the CITI Lab of INSA-Lyon (Villeurbanne, France). This PhD will be conducted under the supervision of Romain Michon (Inria) and Pierre
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. The project proposes an innovative approach to model sea ice dynamics from the ice floe scale to the basin scale, leveraging hybrid data assimilation and machine learning methods to shape a physically robust
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interdisciplinary, and together we contribute to science and society. Your role Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms