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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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parallel computing techniques including working in the cloud. Preferred Qualifications Education: No additional education beyond what is stated in the Required Qualifications section. Certifications
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parallel computing techniques including working in the cloud. Preferred Qualifications Education: No additional education beyond what is stated in the Required Qualifications section. Certifications
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chemical processes in complex condensed-phase systems, e.g., aqueous solutions, semiconductor interfaces, and polymers. These complex condensed-phase systems will be treated using first-principles machine
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closure modeling and/or high performance computing environments (MPI, CUDA) • Expertise in software development and computing tools (C/C++, python, git, parallel computing, etc.) • Experience with deep
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with ab initio methods. Proficiency in software such as VASP, Quantum ESPRESSO, Critic2 and Gibbs2. Experience in high-performance computing and parallel processing. Additional Qualifications Considered
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will join Andrej Prsa’s research team and work on the PHOEBE code , advancing our understanding of the processes in contact binary stars. In parallel, the applicant will be given an opportunity to teach
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of API design, asynchronous processes, service deployment and delivering highly available services. Define technical direction, and provide leadership for product initiatives. Engage with other leaders in
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Posting Details Do you have questions about the application process? If so, please refer to the Applicant FAQ’s. Posting Details (Default Section) Posting Number: 20231097F Position Title
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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