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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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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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natural language processing and machine learning workflows; (3) experimental design and causal inference (including virtual lab experiments); and/or (4) network or computational modeling. The ideal
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and causal inference (including virtual lab experiments); and/or (4) network or computational modeling. The ideal candidate will have a strong interest in applying these tools to questions of group
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subfields will also be given full consideration. Advanced methodological training is essential; this includes high-level familiarity with causal inference, experimental methods, and econometric techniques
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-level familiarity with causal inference, experimental methods, and econometric techniques. Familiarity with public opinion research and experience working with historical sources would be helpful, but not
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/ Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date machine learning libraries Excellent written and verbal communication skills Track record of publishing
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and performance analysis, Proven track record of publications in relevant IEEE journals and conferences, Strong verbal and written skills in English, Excellent analytical and problem-solving skills and
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faculty at NYUAD and NYU New York, among other international collaborations. The neuroimaging facilities at NYUAD include a 3T Prisma MRI, MEG, EEG, and eye tracking. In addition to supporting the research
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networks Expertise in physical layer and MAC layer design for wireless systems Proven track record of publications in relevant IEEE journals and conferences In addition to the above qualifications, it is