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Field
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following: Data Science, Machine Learning, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data management and collection, causal inference, network analysis, graph
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: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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expertise in controlling and deploying them in practice, as well as in designing coordination strategies for them. Our recent work on RL and graph neural networks (GNNs) demonstrate some of our key research
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Knowledge Graphs. Our key objective is to hire a recently conferred PhD with outstanding qualifications and core values of high standards of excellence in innovative research. Benefits at UTA We are proud
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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the College of Engineering at The University of Texas at Arlington invites applications for a Post-Doctoral Researcher. Fields of interest are limited to Natural Language Processing and Knowledge Graphs. Our
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Science, Statistical Physics, or any other related field. The skills that we are looking for include: Statistical analysis & causal inference Data management, collection & visualization Social network analysis & graph
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include statistical analysis, data management and collection, causal inference, network analysis, graph theory, visualizations, and online tool development. Experience in conducting online controlled
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novel hypotheses, ideas, and develop novel methodologies. Contribute and participate in the authorship of research publications and presentations. Collect, analyze and graph data, conclude research