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Field
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that ingest raw on-chain data (blocks, transactions, smart-contract events) from public blockchains into research-grade databases Developing statistical, graph, and/or machine learning models to study
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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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Developing statistical, graph, and/or machine learning models to study transaction networks, illicit transaction patterns, and DeFi protocol operations Creating and evaluating tools, documentation, and open
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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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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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: 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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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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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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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