A postdoctoral position is available in our research group in computational mechanics with applications in mechanics of materials or biomechanical modeling, where research activities are aimed toward improving the predictive capability of multi-physics simulations. This includes, but is not limited to, developing numerical methods and algorithms for probabilistic methods for quantifying uncertainty, machine learning-based methods for computational mechanics problems, multiscale analysis, fluid-structure interaction or interface modeling, and reduced order models. Example applications are microstructure solidification and residual stress modeling for additive manufacturing, physics informed neural network models for structural and fluid mechanics problems, machine learning models for process-microstructure-property relationships, and simulation of extreme events such as material failure or high strain rate biomechanical response. The ideal candidate is expected to be proficient in a compiled language (e.g., C, FORTRAN, C++), comfortable compiling and using open source codes in a UNIX environment, and familiar with a parallel programming library (e.g., MPI, OpenMP, Cuda). Additionally, the ideal candidate will have a strong theoretical foundation in either numerical methods for solving partial differential equations or stochastic/probabilistic methods for uncertainty quantification.
1. Raissi, Maziar, Paris Perdikaris, and George E. Karniadakis. "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations." Journal of Computational physics 378 (2019): 686-707.
2. Wang, Chengcheng, et al. "Machine learning in additive manufacturing: State-of-the-art and perspectives." Additive Manufacturing 36 (2020): 101538.
process-structure-property; finite elements; machine learning; probabilistic methods; numerical methods; mechanics of materials; multiscale methods