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of Physics (Ref.: 534748). Applicants should possess a Ph.D. degree in Condensed Matter Physics. Experience in numerical techniques and analytical field-theoretical approaches is desirable. Applicants who
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for motivated students in the field of numerical fluid mechanics and multi-physics simulation methods. Be part of change Analyze and optimize existing coupling strategies to improve the efficiency and scalability
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 4 days ago
Website https://jobs.inria.fr/public/classic/en/offres/2026-09839 Requirements Skills/Qualifications Strong mathematical background. Knowledge in numerical optimization is a plus. Good programming skills in
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 6 days ago
Modelling. Your tasks # Development and implementation of numerical and algorithmic methods for the simulation of fluid-dynamical or environmental systems # Research on quantum and hybrid quantum–classical
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and microstructure-based modeling Experience with numerical methods for PDEs Programming skills in Python (knowledge of C++, Fortran or HPC is a plus) Scientific curiosity and critical thinking Ability
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shaping will be central to the study. The numerical model will be based on the boundary element method (BEM) and semi-analytical approaches developed at I2M. The experimental proof-of-concept will leverage
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Computational and Theoretical Condensed Matter Physics in the Department of Physics (Ref.: 534748). Applicants should possess a Ph.D. degree in Condensed Matter Physics. Experience in numerical techniques and
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finding techniques, such as Newton's method, after adapted numerical discretization. There is a challenge in extending existing BVP schemes to reaction-diffusion problems, choosing suitable spatial and
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uniformity, dynamic response, and integration compatibility with modern microelectronic and photonic platforms. The project will combine numerical modelling, optimisation methodologies, and experimental
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and microstructure-based modeling Experience with numerical methods for PDEs Programming skills in Python (knowledge of C++, Fortran or HPC is a plus) Scientific curiosity and critical thinking Ability