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relevant field at the PhD level with zero to five years of employment experience. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX). Strong background in computational image processing and
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group. The term of the positions is typically two years, with the possibility to renew for the 3rd year, contingent on the project process and availability of funds. Recent publication most related
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processes in internal combustion engines (ICEs), such as fuel injection, combustion, heat transfer, etc. Improve, develop, and implement CFD sub-models necessary to enable predictive ICE simulations
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The Chemical and Fuel Cycle Technologies division is seeking a Postdoctoral Appointee to join a multidisciplinary team developing electrochemical reactions and processes in molten salt electrolytes
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national security. With guidance, the appointee will: Develop and implement processes for chemical separations for nuclear-energy relevant and other industrial chemical applications Develop tools and
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to innovative measurement techniques and novel technologies, like superconducting nanowire detectors and pixelized 3D-printed MCP-PMTs, capitalizing on Argonne's multidisciplinary expertise. Application Process
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during the application process. Any further questions regarding this position should be addressed to Dr. Peter Mueller (pmueller@anl.gov ). Review of applicants is ongoing and will continue until
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the cleanroom with standard nanofabrication process flow Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring range
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
mathematics, or a related field Candidates should have expertise in two or more of the following areas: Uncertainty quantification, numerical solutions of differential equations, and stochastic processes
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geometry manipulation with computer-aided design software. Experience with coupling CFD and FEA codes. Knowledge of multi-dimensional code development (in C++/C/Fortran) for two-phase/multiphase flow and