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MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA | North Ryde, New South Wales | Australia | about 10 hours ago
differential geometry, algebraic geometry, etc.) or for computer science (such as machine learning, linear logic, etc.). While the position start date is flexible, the successful applicant must have completed
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 1 day ago
of symbolic and numerical methods, or computational geometry (stratification and triangulation).Solid background in linear algebra and differential equations to address control theory (H∞ methods) and linear
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- Linear Algebra and Probability MATH 133 - Linear Algebra and Geometry MATH 140 - Calculus 1 MATH 141 - Calculus 2 MATH 203 - Principles of Statistics 1 MATH 204 - Principles of Statistics 2 MATH 222
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undergraduate and graduate teaching, as well as excellence in research. Faculty research interests include algebra, analysis, combinatorics, differential equations, geometry, inverse problems, math education
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unsupervised learning. These methods will be based on new classes of partial differential equations on graphs and new graph neural network models based on diffusion processes. The developed algorithms will be
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record commensurate with the rank are required. The Department of Mathematics has broad research strengths across both pure and applied mathematics, with particularly strong activity in algebra, geometry
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teaching and research expertise in an area that strengthens the school’s profile. This includes, but is not limited to Geometric Analysis, Differential Geometry, and Nonlinear Partial Differential Equations
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to apply: Geometry & Topology (Differential Geometry, Low-Dimensional Topology, Symplectic Geometry, etc.) Partial Differential Equations (PDEs) (Linear & Nonlinear PDEs, Harmonic Analysis & PDEs, etc
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the John Templeton Foundation. Gravity from Entropy is a statistical mechanics approach that derives gravity from principles of information theory and differential geometry. The aim of the project is to
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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