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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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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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certification, dramatically accelerating innovation cycles. What you will gain: Expertise in Finite Element Analysis, Scientific Machine Learning, Uncertainty Quantification, and Professional Programming
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interest and solid foundational knowledge in at least one of the research projects Proven competence in computational design workflows, particularly in Rhino and Grasshopper Knowledge of Python, C#, finite
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learning tools for the prediction of composite manufacturing processes. You will work on development of algorithms, custom written codes, application of commercial finite element software and development
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research area: Python or C#, robotics, additive manufacturing, finite element analysis, or computational fluid dynamics. Experience in teaching and co-supervising thesis students at university level
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micro-electro-mechanical device (MEMS) dedicated to the manipulation and measurement of micro-samples, the coupled gripper-sample modelling using numerical analysis and finite element methods, and the
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simulation software developed "in house" known as Fluctuating Finite Element Analysis (FFEA), and you will learn how to improve this software so that we can watch a molecule walk. Funding Notes The position
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learning methods, featuring regularizing terms involving differential equations, suitably discretized using finite element schemes. Particular care will be devote to be implementation of the proposed methods
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he will assess different finite element schemes for the accurate and efficient solution to the estimation problem. The candidate will also explore numerical/statistical approaches for the uncertainty