Computational Mechanics
Postdoc needed for computational mechanics research using numerical methods, machine learning, and multi-scale modeling to improve multi-physics simulations.
This grant is no longer accepting proposals
NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: May 1, 2025 (Application Deadline)
Funding Amounts: $99,200 stipend plus $3,000 travel allowance; relocation assistance and health insurance available. Typical postdoctoral fellowship duration 2-3 years.
Summary: Postdoctoral fellowship at Naval Research Laboratory for computational mechanics research focused on numerical methods, machine learning, and multi-scale modeling to enhance multi-physics simulations.
Key Information: Open to U.S. citizens and permanent residents with a recent doctoral degree; requires proficiency in compiled languages and parallel programming; application requires contacting a research adviser prior to applying.
Description
This postdoctoral fellowship opportunity is offered by the Naval Research Laboratory (NRL) under the NRC Research Associateship Programs. The research focus is on computational mechanics with applications in mechanics of materials and biomechanical modeling. The goal is to improve the predictive capabilities of multi-physics simulations through advanced computational techniques.
Research areas include:
- Development of numerical methods and algorithms for probabilistic uncertainty quantification.
- Machine learning-based approaches for computational mechanics problems.
- Multiscale modeling techniques.
- Fluid-structure interaction and interface modeling.
- Reduced order modeling.
Example applications involve microstructure solidification and residual stress modeling in additive manufacturing, physics-informed neural networks for structural and fluid mechanics, machine learning models linking process-microstructure-property relationships, and simulations of extreme events such as material failure or high strain rate biomechanical responses.
Ideal candidates should have:
- Proficiency in compiled programming languages such as C, FORTRAN, or C++.
- Experience compiling and using open-source codes in UNIX environments.
- Familiarity with parallel programming libraries like MPI, OpenMP, or CUDA.
- A strong theoretical foundation in numerical methods for partial differential equations or stochastic/probabilistic methods for uncertainty quantification.
The fellowship provides a comprehensive research experience with mentorship, access to state-of-the-art facilities, and opportunities to collaborate with leading scientists.
