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models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing
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applications. With guidance, the appointee will: Develop advanced multiscale, multiphysics simulation tools relevant to the modeling of processes involving combined nuclear, chemical, and electrochemical
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of molecular reactions occurring at the surface of various materials. In addition, computational fluid dynamics (CFD) simulations combined with microkinetic modeling will be carried out to study the heat
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simulations, predict emerging and new variants of interest in SARS-CoV-2 Integrating high-throughput deep mutational scanning and reverse genetics workflows to support pandemic bio-preparedness. As part of
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decarbonization applications. With guidance, the appointee will: Develop advanced multiscale, multiphysics simulation tools applicable to the modeling of chemical processes and equipment relevant to chemical
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to solve challenging problems in the microelectronics area. Note: Synthesis of bulk materials, first-principles simulations/modeling, and organic or bio-related areas are not in consideration
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computer science or related computational engineering disciplines. Experience with simulation frameworks for complex computer systems and architectures. Some knowledge of accelerator (CUDA, SYCL, HIP) and scientific
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(electrochemistry, materials synthesis, or characterization) or computational simulations perspective, is required. Proficiency in Python programming is required. Familiarity with REST APIs is desirable. Master’s