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Understanding and modeling material behaviour is central to many engineering challenges. This fully funded Doctoral student project, “Material Model Discovery from Physics-Encoded Neural Networks
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the development and refinement of Monte Carlo simulation generators to accurately model neutrino interactions with various target materials. Detailed comparisons of these simulations to data from existing neutrino
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Healthcare Monitoring We welcome applications from those with expertise in or across these disciplines: Computational materials modeling: DFT, molecular dynamics, phase-field modeling, or multiscale
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-based interatomic potentials, and active learning strategies for identifying materials with high thermoelectric efficiency (ZT figure of merit). Where to apply Website https://emploi.cnrs.fr/Candidat
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following responsibilities: ● Adapt, enhance, and validate an existing bottom-up/activity-based emissions model to examine maritime shipping emissions at a global scale. See https://www.sciencedirect.com
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approaches applied to different science domains, such as chemistry, materials, physics, imaging, drug discovery, climate/weather forecast, etc. Knowledge of implicit deep learning models (NERF, neural
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Develop and validate material models and performance predictions using simulation tools Integrate materials into injection moulding and extrusion processes through prototyping Collaborate with
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1,400 students (including 250 foreign students) and 380 staff members. The school has three high-level scientific and technological research and teaching centers working in the fields of materials and
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aims to understand the smoldering fire behavior, its propagation within the material and the influencing parameters. These tests will allow to validate the model developed. Influence of smoldering
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practical skills in numerical modelling. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR5510-PETGRI-001/Default.aspx Work Location(s) Number of offers available1Company