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Field
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Classification Title: Post Doctoral Associate Job Description: A Post-doctoral Associate in Social Network Analysis position is available for up to 2 years to work within the NSF supported project
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Requisition Id 14891 Overview: We are seeking a Postdoctoral Research Associate that will focus on elevating scanning transmission electron microscopy (STEM) and scanning probe microscopy (SPM) efforts within the Center for Nanophase Materials Sciences (CNMS) through hardware developments. As...
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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
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position to work on development and scaling of the data infrastructure and software for AI applications on supercomputing systems and AI testbed systems. The postdoc will work on multimodal data management
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of Nevada, Reno (UNR) invites applications for a Postdoctoral Position: Supply Chain Modeling for Water Networks and Lithium Supply Chains. We are seeking a dedicated and innovative postdoctoral researcher
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experts across system software, power management infrastructure, performance characterization, networking, and novel computer architectures and accelerators. It will also involve collaboration with leading
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to join our research team focused on the development and implementation to build a Long Range (LoRa) network system tailored for agricultural applications and conducting experiments in our 45-acre
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About the Opportunity SUMMARY The lab of Professor Albert-László Barabási is looking for Postdoctoral Research Associates in the area of network science, nutrition, biological networks, machine
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. The appointment is for one year, renewable for a second year given the availability of funds. The focus is on fundamental properties of simulated neural networks, like neural scaling laws, as they manifest in
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multiple brain regions interact with each other, or networks. Furthermore, we are focused on identifying factors affecting the plasticity of these representational networks, and the degree to which they can