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developing advanced, technologically driven solutions for integrated monitoring and decision-making systems for complex structures and infrastructures. We invite applications for a postdoctoral research
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systematic perturbation systems for understanding brain network Position Description: We are seeking a highly motivated postdoctoral scholar to join our interdisciplinary research team. The successful
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understanding of biochemistry and materials chemistry with the ability to perform assay development. The successful candidate must be able to develop independent protocols for complex novel assays for multiple
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place technologies and to develop digital twin algorithms to assist clinicians in developing treatment plans. Analyzes complex sensor data, works with a multidisciplinary team to develop health digital
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will contribute to the development of novel concepts and proposal writing, while efficiently addressing complex challenges. Responsibilities will include writing reports, authoring publications, drafting
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of improving human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five centers and institutes that focus
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research projects aimed at (1) understanding the molecular mechanisms by which trabecular meshwork protein modifications induce fibrosis by transforming epithelial cells to myofibroblast-like cells (2
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on applications to complex, dynamic systems. Design and simulate feedback control algorithms for thermo-mechanical systems and related applications. Collaborate with faculty and student teams to support control
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About the Opportunity SUMMARY The Network Science Institute (NetSI) is hiring one Postdoctoral Research Associate to join the newly formed Complexity & Society Lab (the “&-Lab”). The &-Lab is an
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate