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Postdoctoral Researcher in Computational Solid Mechanics
2 months ago
Join the Lawrence Livermore National Laboratory (LLNL) as a Postdoctoral Researcher specializing in Computational Solid Mechanics. We are dedicated to fostering a diverse and inclusive workforce that values the unique contributions of each individual.
Key Responsibilities:
- Develop and refine constitutive models and computational techniques to simulate the behavior of solid materials under dynamic conditions.
- Analyze experimental data to create suitable models that capture strength, damage, rate, and thermal effects in various engineering materials.
- Enhance numerical methods to address mesh dependency issues in large-deformation simulations, including brittle damage and thermal softening.
- Utilize numerical optimization techniques to parameterize complex material models based on available data and physical constraints.
- Conduct mesoscale simulations to explore the response of composite and heterogeneous materials under dynamic loading.
- Perform large-deformation solid mechanics simulations to validate models against experimental results.
- Collaborate with experimental teams to interpret data and prioritize new experiments aimed at reducing modeling uncertainties.
- Present research findings at conferences and publish in peer-reviewed journals.
Qualifications:
- Ph.D. in mechanical, civil, or materials engineering, applied mathematics, condensed matter physics, engineering mechanics, computer science, or a related discipline.
- Experience in the application or development of numerical methods for solid mechanics, such as FEM, MPM, SPH, and hydrocodes.
- Proficiency in programming and code development for scientific or engineering applications.
- Strong communication skills for effective collaboration in a multidisciplinary environment.
- Ability to obtain and maintain a U.S. Department of Energy Q-level security clearance, requiring U.S. citizenship.
Desirable Qualifications:
- Familiarity with numerical methods for large-deformation solid mechanics.
- Experience in developing constitutive models.
- Knowledge of dynamic response theories and experimental methods related to materials under shock loading.
- Proficiency in C++/Python, particularly with large legacy codebases or GPU programming.
- Experience with high-performance computing platforms and Linux systems.
- Knowledge of optimization methods for parameterizing physics models.
This position offers an opportunity to contribute to innovative research at LLNL, where your expertise will help address significant challenges in solid mechanics.
