As a Modeling Engineer, you will develop and refine advanced finite element models to support structural, thermal, and materials-focused analyses. You will run complex simulations, interpret results, and turn those insights into practical design guidance. You will collaborate with teams and material scientists to align modeling approaches with real-world testing, improve predictive accuracy, and influence product development. You will also apply optimization methods, support validation efforts, and give your findings through reports and presentations.

PPG offers excellent Medical, Dental, & Vision Benefits; matching 401k + retirement savings; and Paid Vacation, Holiday pay.

Key Responsibilities

Lead the development, validation, and enhancement of high-fidelity finite element models for mechanics analysis and potentially heat conduction analysis. Produce advanced model that capture multi-physics coupling, material nonlinearity, and complex boundary interactions.
Develop and guide linear/nonlinear structural, thermal, impact, and fatigue analyses using commercial FEA tools (e.g., ABAQUS, ANSYS, LS-DYNA, COMSOL). Interpret simulation outcomes with scientific rigor to uncover fundamental failure mechanisms, improve design decisions, and establish predictive design guidelines.
Develop and calibrate constitutive models for metals, polymers, composites, or coatings based on experimental data. Implement custom material subroutines (e.g., UMAT/VUMAT).
Lead research efforts in model accuracy improvement, numerical stability, and computational efficiency. Explore latest numerical methods (e.g., cohesive zone modeling, phase-field fracture, multi-scale modeling) to expand simulation capabilities.
Data-Driven & Hybrid Modeling: Integrate data analytics, machine learning, and statistical inference techniques with traditional FEA to build hybrid physics–ML models. Develop surrogate models, reduced-order representations, or Gaussian Process regressors to accelerate simulation cycles and support uncertainty quantification. Use experimental databases and simulation data to extract governing relationships, automate parameter calibration, and ensure predictive digital twins.
Collaborate with material scientists to design validation experiments, compare simulation predictions to physical test results, and refine models for improved accuracy and predictive power.
Work with design, manufacturing, and product development teams to translate engineering insights into applicable design or process changes.
Prepare technical documentation, analysis reports, and presentations summarizing modeling work.

Qualifications

PPG offers excellent Medical, Dental, & Vision Benefits; matching 401k + retirement savings; and Paid Vacation, Holiday pay.

Key Responsibilities

Lead the development, validation, and enhancement of high-fidelity finite element models for mechanics analysis and potentially heat conduction analysis. Produce advanced model that capture multi-physics coupling, material nonlinearity, and complex boundary interactions.
Develop and guide linear/nonlinear structural, thermal, impact, and fatigue analyses using commercial FEA tools (e.g., ABAQUS, ANSYS, LS-DYNA, COMSOL). Interpret simulation outcomes with scientific rigor to uncover fundamental failure mechanisms, improve design decisions, and establish predictive design guidelines.
Develop and calibrate constitutive models for metals, polymers, composites, or coatings based on experimental data. Implement custom material subroutines (e.g., UMAT/VUMAT).
Lead research efforts in model accuracy improvement, numerical stability, and computational efficiency. Explore latest numerical methods (e.g., cohesive zone modeling, phase-field fracture, multi-scale modeling) to expand simulation capabilities.
Data-Driven & Hybrid Modeling: Integrate data analytics, machine learning, and statistical inference techniques with traditional FEA to build hybrid physics–ML models. Develop surrogate models, reduced-order representations, or Gaussian Process regressors to accelerate simulation cycles and support uncertainty quantification. Use experimental databases and simulation data to extract governing relationships, automate parameter calibration, and ensure predictive digital twins.
Collaborate with material scientists to design validation experiments, compare simulation predictions to physical test results, and refine models for improved accuracy and predictive power.
Work with design, manufacturing, and product development teams to translate engineering insights into applicable design or process changes.
Prepare technical documentation, analysis reports, and presentations summarizing modeling work.

Qualifications