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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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to uncover novel regulators and mechanisms of neuromodulation. Develop computational approaches to model spatiotemporally resolved signaling networks. Academic environment: Stanford University provides a
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neural networks, like neural scaling laws, as they manifest in metrics specific to robotics and impact robotics applications. The thrust of the research is multidisciplinary. The successful candidate will
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fundamental properties of simulated neural networks, like neural scaling laws, as they manifest in metrics specific to robotics and impact robotics applications. The thrust of the research is multidisciplinary
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information here. Explore the additional perks of working at CSU here . For the total value of CSU benefits in addition to wages, use our compensation calculator . Lastly, click here for more information about
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oncology, infusion, radiation, proton therapy and related services, and network affiliations with hospitals in five states. Together, our fully integrated research and clinical care teams seek to discover
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of national research infrastructures. The ideal candidates will have a PhD in a discipline closely related to computational social science by date of appointment (e.g. network science, computer science, data
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, and the program will help them develop strong research networks by facilitating collaboration among the postdoctoral cohort and by ensuring that they have ample opportunities to interact with the whole
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plasticity metrics derived from functional MRI data. Investigate developmental differences in infant brain functional networks. Support generation and testing of improvements for code bases for the analysis
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
, large-scale computational science, and simulation of networked physical systems Familiarity with techniques for sensitivity analysis and handling high-dimensional problems Experience in power grid