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the development and coupling of numerical methods for solid mechanics modeling Experience in digital rock technology, including advanced imaging and related analysis Experience in the performance of high pressure
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fields: Material Science, thermal simulation, Metallurgy, Solidification of alloys,... School - Location: Centrale Lille Institute Laboratory: LaMcube Web site: http://lamcube.univ-lille.fr/ Name of
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computing environments. Experience with numerical modelling techniques, such as finite difference, finite element, or spectral element methods. Interest in inverse problem formulation and solving and/or
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-like molecules (Fragment-Based Drug Discovery) has strongly modified the generation of therapeutic compounds1. The method consists in identifying small organic compounds (fragment hits) that bind
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Efficient and Reliable Numerical Solution of Dynamic Optimization School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Yuanbo Nie Application Deadline: Applications
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Functional Theory (DFT) Familiarity with artificial intelligence methods Good knowledge of electronic structure methods Experience with Linux, Git and related tools Knowledge in the field of high-performance
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complementary data from other Mars missions to strengthen current models and provide comparative insights that enhance research conclusions from Hope observations. Develop Machine Learning methods and run
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requirements and focusing on data-value maximisation. This project will utilise innovative machine learning methods and tools from process systems engineering to simultaneously optimise product quality and the
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simulation of time dependent non-linear PDEs has emerged as a key technology. A main task of this employment is the development and analysis of numerical methods for wave propagation problems. Particular focus
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | about 2 months ago
-Carrillo et al. 2024). Champaney et al., 2022. Int J Mater Form 15, 31. https://doi.org/10.1007/s12289-022-01678-4 Chinesta et al., 2020. Arch Computat Methods Eng 27, 105–134 https://doi.org/10.1007/s11831