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Posting Information Posting Number FAE1689 Advertised Title Assistant Professor in Combinatorics & Graph Theory Campus Location Main Campus (Memphis, TN) Position Number 001954 Category Full-Time
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University of California San Francisco | San Francisco, California | United States | about 13 hours ago
Marshall Lab - Junior Specialist Biochemistry and Biophysics UC San Francisco Will carry out mathematical analysis of mitochondrial network structure and dynamics, using tools of algebraic graph theory and
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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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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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) 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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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
interfaces. Topics of interest include: Planar and geometric graph algorithms Approximation and parameterized algorithms Clustering, embeddings, and structural graph theory Computational complexity and
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work, studying in disciplines ranging from atomic physics and graph theory to medieval literature and blind rehabilitation. Of 101 graduate offerings available, 30 lead to a doctoral degree. Connections
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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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-time data acquisition during production, consulting, and prototype manufacturing. Graph neural networks provide an opportunity to operate on Mesh structured data utilized in Finite Element Method (FEM