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chain network analysis and geospatial modeling. The successful candidate will have strong data science skills, including experience working with large, complex data from varied sources, and machine
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. Receiving mentorship in grant writing and navigating the publication process. Participating in career development activities through Cornell's mentoring resources and professional network. Overall
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evaluate distribution networks in OpenDSS; investigate feeder- and system-level impacts of DERs (e.g., load flow, hosting capacity, voltage regulation). Develop and refine T&D co-simulation platforms (e.g
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, graph neural networks) to match questions to relevant geodata workflows; integrate semantic web technologies (e.g. RDF, SPARQL, OWL), large language models and graph neural networks into the reasoning
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individual processes to entire manufacturing systems. The positions will all focus on factory and line level, where three research topics are defined: 1) Conceptual design principles and methods for resilient
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, human-systems interactions, networks science, information, decision complex networks, dynamic data-driven application systems, the science of information computation and fusion, systems and software
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the Interpretable Machine Learning Lab (https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in collaboration with other researchers on machine learning tools that help humans make better
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skills in distributed machine learning, networked systems, data management, and/or software engineering are recommended for this position. The post requires strong programming and system development
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present research in conferences. Network and develop collaboration with other groups and divisions internally and with other national labs, industry, and utilities. Position Requirements Recent or soon-to
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., omics or clinical data), using statistical methods, pathway/network analysis or machine learning. The candidate will conduct integrative analyses of biomedical datasets, with a focus on omics data