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responsibility of developing predictive tools based on machine learning for the analysis and interpretation of Raman vibrational spectra applied to battery materials. The successful candidate will design and
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collaboration with the project's principal investigator and partner laboratories, the postdoctoral researcher recruited at Praxiling will be responsible for the linguistic analysis and modeling of the multimodal
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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform
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structures and corresponding images) needed for training and validating deep learning (DL) models. Work closely with members of the ICMN nanostructures group or external collaborators. Communicate research
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existing structural and functional MRI data, acquire new data in collaboration with clinical researchers, and prepare publications and conference presentations. - Study preparation - Data acquisition (MRI
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Description CNRS offers a 18-month fixed-term contract researcher position to work on the recently funded project ACCTS (“Assessing cirrus cloud thinning strategies by learning from aerosol-cirrus interactions
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collaboration between the Exa-SofT and the Exa-DI projects and better support multi-linear algebra and tensor contractions in exascale CSE applications and Machine Learning. As part of the collaborative process
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of results at conferences - interaction with team members and international collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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elucidating the molecular and cellular mechanisms of the late phase of long-term potentiation (LTP), a key process in learning and memory. The project is based on the development and use of an innovative