66 post-doc-in-wireless-communication-and-networks-2016 Postdoctoral positions at Argonne
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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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structural models and compute electronic and vibrational properties. Develop and train neural-network or other machine-learned interatomic potentials to enable large-scale molecular dynamics (MD) simulations
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to assess evolving risks in coastal-urban regions. Other key responsibilities include: Mesh design and high-resolution data utilization. Develop and refine high-resolution barotropic ocean meshes along U.S
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for molecular design (e.g., DFT, MD, AI/ML) is desired, but not required. · Strong oral and written communication skills are required. · Ph.D. in an experimental discipline, such as chemistry, materials
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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(BCDI), Laue microdiffraction, ptychographic laminography, and X-ray photon correlation spectroscopy (XPCS) to study strain, dislocation networks, voids, and interfacial morphology. Develop in-situ and
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, PyTorch, JAX etc. Experience with modern AI concepts such as large language models (LLMs), vision-language models (VLMs), model context protocol (MCPs), and the development of agentic AI tools. Skill in
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. Understanding of high-order methods for fluid flows. Understanding of turbulence, boundary layer flows, multi-phase flows, chemical kinetics, combustion, and detonations. Experience in mesh generation with
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, including communication, networking, and leadership. Position Requirements To perform the essential functions of this position successful applicants must provide proof of U.S. citizenship, which is required
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Biology: Strong background in systems biology and regulatory network modeling Interdisciplinary Collaboration: Experience working across disciplines with computational biologists, computer scientists, and