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Field
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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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-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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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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• Relational (deep) learning • Graph neural networks • Reinforcement learning These domains are central to our mission of advancing intelligent systems through principled, data-driven approaches
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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 literature and blind rehabilitation. Of 101
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the theory of quantum graph states. Additional expertise in computational methods would be useful but is not necessary. The Postdoctoral and Senior Research Associate positions will also involve
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atomic physics and graph theory to medieval literature and blind rehabilitation. Of 101 graduate offerings available, 30 lead to a doctoral degree. Connections working at Western Michigan University More
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graph theory. Qualifications Candidates with a Ph.D. in any area of cognitive neuroscience broadly defined (e.g., Psychology, Neuroscience, Computer Science, or a related field) are welcome to apply
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. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains. The position is hosted in the Leibniz Junior Research Group