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network structure and dynamics, using tools of algebraic graph theory and statistical mechanics, with a focus on determining how mitochondrial dynamic processes like fission and fusion give rise
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main areas of research are machine learning, distributed systems, and the theory of networks. Within these three areas, we are currently working on several projects: graph neural networks, natural
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) Experience with modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow) Background in at least one of the following: graph learning, scientific computing, surrogate modeling, or ML theory Interest in
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robust feedback control theory for nonlinear dynamical systems using input-output operator theory and graph separation theory. Excellent knowledge of input-output dissipativity theory, input-output
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of focus include random graphs and trees (combinatorial properties, scaling limits, coalescent and branching structures, random matrix theory), stochastic and Lévy processes in infinite-dimensional spaces
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of focus include random graphs and trees (combinatorial properties, scaling limits, coalescent and branching structures, random matrix theory), stochastic and Lévy processes in infinite-dimensional spaces
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Quantum computing and Graph theory In this role, you will be responsible for conducting research on graph theoretic approaches to design quantum photonic experiments. Additionally, the position involves
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, computational fluid dynamics and material science, dynamical systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics
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Description Join us in seeking exciting new developments using graph theory in nearest neighbor models for active matter! Do you enjoy working with graph theory, and seeing how functions on graphs can inform
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of functions and graphs, polynomial functions, rational functions, exponential and logarithmic functions and trigonometric functions. Corresponding Alberta High School Equivalent: Mathematics 30-1. MATH 0132