Two post-doctoral positions are immediately available at the NSF-funded NHERI Center for Computational Modeling and Simulation at UC Berkeley. The SimCenter has recently been renewed through 2025 to provide open-source software that seeks to provide integrated scientific workflows for natural hazards engineering researchers. Considered hazards include earthquakes, hurricanes, and tsunamis and their effects on isolated structures all the way up to regional collections of structures and the communities in these regions. The SimCenter also provides educational and training activities to new researchers. This work takes place within the context of supporting efforts to quantify, on a granular basis, current exposures to hazards and to the development of a resilient and sustainable infrastructure.
The SimCenter seeks well qualified, motivated, software development team members with interest and experience in simulation-based approaches to Natural Hazards Engineering on scales ranging from single structures to metropolitan areas. While this high impact effort involves investigators across the United States and abroad, current openings are located at UC Berkeley. Expertise is being sought in the following areas:
The SimCenter seeks a postdoctoral candidate with expertise in Computational Fluid Dynamics (CFD). The successful candidate will have extensive experience with LES modeling of turbulent flows, including the validation of LES results against wind tunnel measurements. Additional desired qualifications include background in performance-based wind engineering, windstorms, and background or interest in Uncertainty Quantification (UQ) methods and their application to CFD.
The SimCenter seeks a postdoctoral candidate to advance the development of its machine learning and data mining tools for the development of inventory data used in predictive engineering models. The successful candidate will have experience in state-of-the-art deep learning algorithms, including uncertainty estimation of predictions from machine learning models. As the SimCenter inventories and predictive models are generated through diverse approaches, working backgrounds in data harvesting (including crowdsourcing), and metadata extraction from different sources (photographs, videos, scanned document images, point clouds, etc.) via automated processing, detection, and classification are required. Additional background in geographic information systems and photogrammetry is highly desirable. The ideal candidate will have applied the tools and skills mentioned above to applications related to civil engineering and/or natural hazards.
For both of the open positions listed above, advanced skills in computer programming are required. Candidates should have demonstrated experience (35 years) in two or more of the following: (1) software engineering and software design; (2) scientific workflow systems; (3) community software development, version control, documentation, and maintenance; (4) proven knowledge of computer languages used in scientific computing (e.g., C, C++, Modern Fortran) and knowledge of scripting languages used in scientific data processing (e.g., Matlab, R, Python); and (5) proven experience/knowledge of parallel and multi-threaded programming (e.g., MPI, OpenMP, CUDA) and I/O tools for parallel access and management of large datasets. In addition, candidates must have excellent English language skills, design sense, and team spirit. Finally, candidates need to be able to work in a highly interdisciplinary environment and eager to interact with SimCenter users.
Candidates should submit their application materials as a single pdf file (< 5 MB), including a short motivational letter, their CV, and copies of academic credentials (BS, MS, and PhD) with attention to Professor Sanjay Govindjee (s_g@berkeley.edu). Applicants are encouraged to submit their materials as soon as possible. Review of applications will begin on October 29, 2021 and will continue until the positions are filled. Successful candidates are expected to start working in Berkeley no later than January 2022.
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