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the development of high-fidelity finite element models to investigate surface wave propagation in soft biological tissues, forming the foundation for subsequent statistical and machine learning
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of high-fidelity finite element models to investigate surface wave propagation in soft biological tissues, forming the foundation for subsequent statistical and machine learning frameworks that integrate
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
of the following areas: Large-deformation numerical modeling (e.g., Coupled Eulerian-Lagrangian (CEL), Material Point Method (MPM), or advanced Finite Element Methods). Physical modeling of tunnel
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apparatuses, free space and fiber optics, precision interferometry, quantum optics, analog and digital controls, low noise electronics, simulation and modeling, finite element modeling, and computer programming
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of experimental data - Molecular Dynamics and Finite Element calculations. . Metallic nanoparticles (NPs) exhibit unique physico-chemical properties departing from those of bulk materials, primarily due
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advanced modelling approaches—such as finite element analysis —to capture the nonlinear, multi-physics nature of soft materials. By integrating experimental data and validating simulations, your work will
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in a finite element code. Study the different dynamic regimes depending on the model parameters. Where to apply Website https://seuelectronica.upc.edu/en/procedures/call-for-recruitment-of-pdi-postdo
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developed using finite element analysis (FEA) between LMGC, ICube and LEM3 Labs to model the behaviour of Wharton's jelly samples in an ex vivo and in vivo context. Predictive tools, based on previous models
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Eligibility criteria Numerical analysis and finite element method Solving anisotropic problems Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7340-SOPBAU-024/Default.aspx Work Location
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heavy software development component. The successful candidate will perform research in the application of machine learning (ML) techniques to the finite element method (FEM) in the context of composites