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thermodynamic cycles by combining two complementary approaches: - Generative models derived from artificial intelligence, capable of proposing new process architectures; - Superstructure-based optimization
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implement and train neural network architectures, including Physics-Informed Neural Networks (PINNs), in order to integrate physical constraints into the learning process and improve the identification and
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) nanosheets for low-pressure hydrogen storage applications. This position involves close collaboration with academic and industrial partners to design innovative nanomaterials and optimize their performance
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) focuses its research on Solid State Chemistry, Materials Science and Chemistry and Process: designing, preparing, shaping and characterizing materials in order to discover, control and optimize specific
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switching in corresponding devices. In this project, we first plan to make optimized engineering of metal stacking involving light elements promoting the required orbital polarization and demonstrate
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skills: Python programming; solid background in optimization and/or inverse problems; strong interest in signal processing, experimental data and physics-based modelling. • Important assets: experience
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(UV-Vis, IR, Raman…), microscopies (TEM, SEM), electrochemistry, and other relevant physico-chemical techniques. Chemical process development: optimization of reaction conditions, analysis of yield and
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research unit of CNRS, Grenoble INP, and Université Grenoble Alpes, is a center of excellence in materials science and innovative processes. It spans diverse fields—from metallurgy to nanomaterials and
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of deep learning in many disciplines, particularly computer vision and image processing. Consequently, coding architectures based on deep learning and end-to-end optimization have been proposed [Ding 2021
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registered at the MIMME Doctoral School (https://mimme.ed.univ-poitiers.fr/ ). Institut Pprime is a dedicated research unit (UPR) of the CNRS. Its scientific activities span a broad spectrum ranging from