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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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the command line. Background in structural biology, molecular modeling, protein dynamics, and understanding of ensembles. Have experience with large-scale computations on compute clusters, bonus points
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and resilient materials and structures. By combining numerical modeling, laboratory experiments, and theoretical analyses, we seek to link microscopic processes with the macroscopic behavior of both
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by the CNRS, the postdoctoral researcher will be responsible for contributing to the development of advanced methodologies for predicting crystal structures (CSP) based solely on their chemical
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, or related field - Expertise in advanced quantitative/statistical methods (e.g., generative computational modeling, multilevel and/or structural equation modeling, univariate and/or multivariate fMRI
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electrogravimetric response. To do this, we will implement a multi-stage approach, combining the development of model electroactive materials, their physico-chemical characterisation, and the advanced exploitation
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value and student learning experience is crucial. Preference will be given to those with experience in qualitative analysis and advanced statistics such as Structural Equation Modelling, Multilevel
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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with deep expertise in polymer synthesis, characterization, and structure–property relationships, and with a passion for pushing the boundaries of polymer research.Responsibilities - Conduct advanced
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: Beijing, Beijing 100190, China [map ] Subject Areas: Condensed Matter Physics / Condensed Matter Physics, Electronic Structure, Strongly Correlated Materials , Condensed Matter Theory , condensed matter