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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database
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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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. 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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situ or even operando (laboratory and Synchrotron), and modeling tools (Monte Carlo, molecular and Brownian dynamics, machine learning), allowing, as often as possible, spatially and temporally resolved
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-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at the interface of machine learning and biology (tools developed by the team: https://github.com/cantinilab ). The team is composed
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for a duration of six years. NumPEx contributes to the design and the development of numerical methods, software components and tools that support future productive European exascale and post-exascale
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the modularity and the high degree of addressability of self-assembled DNA nanostructures as a biosynthetic tool to probe fundamental aspects of biology. The recruited postdoc will develop two main projects: (1
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molecular dynamics simulations of modified nucleosomes - analyze the large data set obtained using various analysis tools, from visualization to automation using machine learning tools - perform QM/MM
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employ hydrodynamic and/or Fokker-Planck codes to simulate the initial plasma expansion leading to the creation of the HOFI channel. Setting up a simulation chain integrating these different tools will be
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experience in scientific programming is a plus. • Experience in constructing Machine Learning potentials would be appreciated. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR5254