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on deep learning coupled with molecular simulations for the investigation of slow variables and transition pathways describing large conformational changes and reactive processes in complex biological
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Eligibility criteria The candidate must have a strong background in materials physics, atomistic simulation, or numerical modeling. Experience in molecular dynamics and handling force fields is required
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generation, targeted atomistic simulations, structural descriptor extraction, and predictive models. The work will aim to establish links between local pore geometry, structural disorder, sodium adsorption
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(pure and metal-doped) and carbon chains in supernova ejecta. • Apply molecular dynamics (MD) methods to simulate the formation of alumina agglomerates. • Use ab initio wave function methods to model
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, while preserving ultimate precision in single-molecule localization and access to key photophysical parameters (fluorescence lifetime, brightness, molecular dynamics). This approach paves the way toward