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degree in mathematics or a related discipline Knowledge of solid mechanics, partial differential equations, asymptotic methods, numerical methods, and/or homogenisation are strongly desired Capacity and
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candidate to work in a team on the interdisciplinary StochMan project that integrates elements of stochastic analysis, geometry, partial differential equations, and computational mathematics. An important
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of phenomena represented by partial differential equations (PDEs). With approximately 100 professors, researchers, engineers, administrative staff, and as many doctoral or post-doctoral students
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the possibility of extension. We are looking for PhDs in Mathematics, with experience in Hyperbolic Partial Differential Equations (PDEs), or more in general, evolution equation in the sense of Petrowsky, and who
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for Postdoctoral Associate positions in the broad research areas of mathematical analysis and partial differential equations (PDEs). While all applicants with a background in the analysis of PDEs will be considered
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, United States of America [map ] Subject Area: Analysis - Partial Differential Equations Appl Deadline: 2025/12/01 11:59PM (posted 2025/11/10, updated 2025/10/10) Position Description: Apply Position Description
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The University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 1 month ago
. Preferred Education Skills and Experience: Preferred qualifications include research and teaching experience in mathematics; a strong research interest in partial differential equations, mathematical physics
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, statistical thermodynamics, and numerical methods for partial differential equations. Experience or a strong interest in the study of stochastic fluid models is required. Knowledge of scientific programming
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for partial differential equations. Experience or a strong interest in the study of Van der Waals-type diffuse interface models is required. Knowledge of scientific programming languages (C-Petsc C) and HPC is
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ReGroStaFT, which develops rigorous mathematical approaches to renormalization and coarse-graining for models from statistical physics, particle systems, and stochastic partial differential equations