- Livermore, California, United States
- Hybrid
- Temporary · Associate
- $123K–$123K per year
- 0–5 years
Develop and apply advanced computational methods for modeling complex physical systems using physics-informed machine learning.
Compensation
$123,048 Annually
Responsibilities
- Develop physics-informed machine-learning methods for computational mechanics and scientific simulation.
- Integrate governing equations, conservation laws, boundary conditions, and material constraints into machine-learning models.
- Develop, train, validate, and evaluate neural networks, surrogate models, reduced-order models, and other scientific machine-learning methods.
- Analyze computational and experimental data to identify physical relationships, model parameters, and constitutive behavior.
- Perform computational mechanics simulations using finite-element, finite-volume, or related numerical methods.
Requirements
- PhD in computational mechanics
- PhD in mechanical engineering
- PhD in applied mathematics
- PhD in computational science
- PhD in physics