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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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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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management, collection & visualization Social network analysis & graph theory Online tool/web development Running controlled experiments Game-theoretic modelling Excellent communication skills in English
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Systems Engineering, Aerospace Engineering, or a related field. Degree must be conferred upon hire. Preferred Qualifications Applied expertise in optimal control, heuristic optimization, graph search
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Engineering, Aerospace Engineering, or a related field. Degree must be conferred upon hire. Preferred Qualifications Applied expertise in optimal control, heuristic optimization, graph search algorithms, and
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, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data management and collection, causal inference, network analysis, graph theory, visualizations, and online tool
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with collaborative research methods, contributing to the lab’s graph-based notetaking and knowledge base. • Explore innovative research dissemination methods, including micropublishing, iterative
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independently. Must be able to organize and manage a varied range of assignments and projects with high efficiency. Able to work with database, graphing, word processing, and statistical computer programs such as
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preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research results collected in accordance with the research protocols as stipulated. Prepare and present
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
knowledge graphs. Your work will support the creation of FAIR-aligned metadata (including emerging standards like Croissant) to ensure data provenance, accessibility, and reuse across translational science