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Field
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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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Expertise in statistics, algorithms, graph-based software. Req Biostatistics skills-specific experience with statistical methods for data collection and analysis. Knowledge of healthcare quality principles
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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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, 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 working
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degrees through the doctoral level. More than 20 percent of its 25,000 students are enrolled in graduate course work, studying in disciplines ranging from atomic physics and graph theory to medieval
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objectives are to describe, analyze, and graph these functions, find models from data or a description and solve contextual problems involving these functions with and without technology. Part 2: Set Theory
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include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date
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-based networks graph-based approaches Bayesian learning information theory Documented strong programming skills (preferably Python), for example with contributions to open-source projects, with an active
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topics such as: neural networks self-supervised learning convolutional neural networks transformer-based networks graph-based approaches Bayesian learning information theory Documented strong programming
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• Relational (deep) learning • Graph neural networks • Reinforcement learning These domains are central to our mission of advancing intelligent systems through principled, data-driven approaches