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Field
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causal networks that govern cellular behavior from large-scale single-cell datasets. Our group has pioneered computational approaches for: Inferring causal networks from Perturb-seq (interventional single
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communications (ISaC) to network design using O-RAN; from statistical learning theory to software defined radio (e.g., USRP) prototypes. The Bradley Department of Electrical and Computer Engineering has both
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Job Title Postdoctoral Research Associate - Social Networks Agency Texas A&M University Department Teaching, Learning And Culture (Tlac) Proposed Minimum Salary Commensurate Job Location College
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/Administrative Internal Number: 6558697 Postdoctoral Research Associate About the Opportunity The Network Science Institute (NetSI) at Northeastern University is a world-leading center for research and education
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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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design 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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design Organization Planning Problem identification and resolution Project management Research Statistical analysis Skills: Machine/Equipment: Calculator Computer network (department or school) Computer
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 44 minutes ago
Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org) A complete application to the NASA Postdoctoral Program includes: Research proposal
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28 Aug 2025 Job Information Organisation/Company University of Massachusetts Medical School Department Human Resources Research Field Biological sciences Medical sciences Computer
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new NIH-funded Center for Excellence in Multiscale Immune Systems Modeling. This position focuses on leveraging and developing new equation learning methods, such as Physics-Informed Neural Networks