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Applications are invited for a Post-Doctoral Research Associate in Analysis of PDE with experience in the study of eigenfunctions of elliptic differential operators. The two-year post is funded by
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work on optimization and optimal control related to partial differential equations with emphasis on new developments related to machine learning and data science. Your profile: • Doctorate related
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
mathematics, or a related field Candidates should have expertise in two or more of the following areas: Uncertainty quantification, numerical solutions of differential equations, and stochastic processes
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of other languages Prior knowledge will be valued in topics of harmonic analysis, geometric measure theory, partial differential equations, or free boundary problems. Previous publications related
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programming skills and experience in C++ and Python. • Knowledge (acquired during a Master's program / Dissertation) in numerical methods and simulation, particularly for partial differential equations. • Basic
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, analytical differential equations) to tackle novel questions on eco-evolutionary dynamics in future scenarios of environmental change. Report results in scientific publication(s) and conference(s). Your
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computationally challenging. To address this, our research employs advanced computational methods to simplify high-fidelity 1-D hydrodynamic models based on Partial Differential Equations (PDEs). This approach
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arising from solid/fluid dynamics/geomechanics, and numerical methods for partial differential equations, especially finite difference methods and finite element methods, both theory and applications. A
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, Python, Julia, or MATLAB Knowledge in numerical methods and simulation, particularly for partial differential equations and finite element methods Basic understanding of mathematical modeling with and/or
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Differential Equations, and Graph Neural Networks. The objective is to measure and predict evolutionary forces and spatial cell interactions in healthy versus cancerous tissues, ultimately identifying