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methods which are critical to ensuring structural performance. For more information please visit our website at http://www.nist.gov/mml/infr.cfm/ . Materials; Alternative energy; Fracture; Mechanics
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machine learning (ML) approaches offer a powerful framework for modeling complex catalytic materials with near ab initio accuracy while enabling simulations at significantly larger spatial and temporal
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The Faculty of Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) invites applications for an Associate Professor of Data-Driven Material Modeling (salary group W2
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NIST only participates in the February and August reviews. There is a growing need for high-performance materials for various technological applications. To address this need, the NIST-JARVIS (https
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and the safety implications arising from these interacting processes. You will use and extend PyBaMM, which is an open-source Python-based battery modelling framework (https://pybamm.org
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system models. In technologically detailed energy system models, these requirements are increasingly leading to challenges due to the rapid techno-economic development of the modeled technologies
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engineering training in the areas of design and development, covering subjects such as material selection, mechanics, dimensioning, and modeling. Where to apply Website https://www.galaxie.enseignementsup
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of computational fluid dynamics (CFD) models describing thermochemical conversion processes of biomass and waste-derived fuels, including combustion, pyrolysis, and gasification. The successful candidate will
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FLAME-GPU accelerated agent-based modelling of material response to environmental and operational loading EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce
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”) on the development of material flow analysis (MFA) methods and digital methods for spatial analysis. Specific tasks comprise: Design and apply digital models for analyzing and simulating circular futures