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We invite applications for a Postdoctoral Appointee to contribute to a growing research program in process systems modeling and optimization for clean energy, critical materials, and advanced
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The Interfacial Processes Group of the Chemical Sciences and Engineering Division is seeking a Postdoctoral Appointee. The successful candidate will perform research on the structure and reactivity
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. The project will involve development of novel parallel algorithms to facilitate in-situ analyses at-scale for multi-million and multi-billion atom simulations. In this role, you can expect to work on enhancing
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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
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. Knowledge of multi-dimensional code development (in C++/C/Fortran) for two-phase/multiphase flow and turbulent combustion applications, and parallel scientific computing. Experience in geometry manipulation
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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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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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optimization, with experience in adaptive routing and SDN technologies. Proficiency in programming languages such as Python, C/C++, and experience with parallel computing frameworks. Effective written and oral
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completed Ph.D. within the last 5 years in Computational Biology, Bioinformatics, Machine Learning, Artificial Intelligence, Virology, or a related field Strong programming skills in Python, R, or Julia, with
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Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred Skills and Qualifications: Experience with high-performance computing and parallel computing Familiarity with data