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Posting Summary Logo Posting Number RTF00185PO26 USC Market Title Post Doctoral Fellow Link to USC Market Title https://uscjobs.sc.edu/titles/156387 Business Title (Internal Title) Post Doctoral
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Posting Summary Logo Posting Number RTF00150PO26 USC Market Title Post Doctoral Fellow Link to USC Market Title https://uscjobs.sc.edu/titles/156387 Business Title (Internal Title) Post Doctoral
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approaches to laboratory group Maintain detailed written record of work performed Perform analysis of experiment results and findings, including data collection, entry, statistical analysis, and graph creation
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Posting Summary Logo Posting Number RTF00139PO26 USC Market Title Post Doctoral Fellow Link to USC Market Title https://uscjobs.sc.edu/titles/156387 Business Title (Internal Title) Post Doctoral
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, immunoblotting, RNA extractions, RT-qPCR . The candidate is expected to be familiar with standard software (MS Office Suite, Adobe Suite, Statistical and Graph Software packages i.e. Graphpad Prism) Ability
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computationally efficient reduced-order dynamical systems on graph with modern power grid systems as an application. Education and Experience: Applicants must have recently completed a Ph.D. and have exceptional
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systems architecting AI/ML-driven clinical and operational decision support Digital health and learning health systems Healthcare operations, resource allocation, and workflow optimization Network, graph
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
Design in the Design Computation and Digital Engineering (DeCoDE) laboratory at MIT under the guidance of Dr. Faez Ahmed (https://decode.mit.edu ). Will be responsible for researching, designing, and
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., MATLAB, Python, R) preferred. Solid background in signal processing, statistics, or computational neuroscience, preferred. Outstanding presentation skills, preferred. Experience in graphing, statistical
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accuracy in link-tracing designs (e.g. Respondent driven sampling) Partial graph data collection strategies for networks (e.g. Aggregated Relational Data) Large scale models for anomaly detection on graphs