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This PhD project will investigate partial differential equations arising from kinetic models, with a particular focus on variational methods. The research will explore how variational structures
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written in terms of differential equations. In addition to leading their own research projects, the appointed candidate will have the opportunity to contribute to the projects of PhD students in the group
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conduct research on numerical methods for partial differential equations, with a particular focus on applications in engineering, physics, life sciences, and earth sciences. The research will focus
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, Differential Geometry, and Nonlinear Partial Differential Equations. You will need to have appropriate skills and qualifications as outlined in the position description, including a PhD in Mathematics. If
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methods for differential equations and scientific machine learning; geometric deep learning, manifold learning, equivariant methods, high-dimensional geometry, and structure-aware learning; and the
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 2 months ago
: Course Number and Title: MAT244H5F LEC101 Differential Equations I Course Description: Ordinary differential equations of the first and second order, existence and uniqueness; solutions by series and
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 2 months ago
: Course Number and Title: MAT244H5S LEC101 Differential Equations I Course Description: Ordinary differential equations of the first and second order, existence and uniqueness; solutions by series and
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The research group of Prof. Taoufik Hmidi at New York University Abu Dhabi (NYUAD) is seeking a Research Scientist to conduct research in partial differential equations (PDEs), mathematical fluid
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of mathematics and promotes interdisciplinary and intradisciplinary cooperation. https://mathematik.univie.ac.at/en/research/applied-and-computational-partial-differential-equations/ Doctoral degree/PhD Two years
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-informed learning, inverse problems, uncertainty quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning, manifold learning, equivariant