Postdoc: Advanced Computational Methods & Simulation

GE Vernova, Inc.; Elgin Township (IL); On-site; USD 89,000 - 149,000; Full time

Benefits offered by this job

Medical coverage

Dental coverage

Vision coverage

Prescription drugs

Retirement plan

Tuition assistance

Paid parental leave

Life insurance

Job summary

GE Vernova Advanced Research is seeking a Post Doctoral scholar to advance computational methods and simulation technologies for next-gen energy platforms. The role focuses on physics-based modeling, high-fidelity numerical methods, data-driven modeling, and scientific machine learning applied to power generation and electrification domains.

The successful candidate will drive innovation in simulation capabilities, enable digital twin development, and collaborate with cross-disciplinary teams to

Qualifications

• PhD in Mechanical Engineering, Aerospace Engineering, Applied Mathematics or related field.
• 2+ years post-doctoral research in industrial or laboratory settings.
• Strong fundamentals in fluid mechanics, aerodynamics, turbulence, heat transfer and combustion.

Responsibilities

• Develop advanced computational models and simulation tools for complex physical systems (multiphysics, multiscale).
• Extend high-fidelity methods (CFD, heat transfer, reactive flows) using commercial and proprietary software.
• Create novel numerical algorithms, including ML-augmented simulations and ROMs.
• Perform verification/validation to ensure model accuracy against data.
• Collaborate with experimentalists and domain experts for data integration and calibration.
• Advance GE Vernova’s digital engineering capabilities and digital twins.

Skills

Python

JAX

PyTorch

TensorFlow

FORTRAN

C++

High-performance computing

Education

PhD in Mechanical/Aerospace/Applied Math or related

Tools

ANYSYS

OpenFOAM

COMSOL

Abaqus

Job description

GE Vernova Advanced Research is seeking a Post Doctoral scholar to advance computational methods and simulation technologies for next-gen energy platforms. The role focuses on physics-based modeling, high-fidelity numerical methods, data-driven modeling, and scientific machine learning applied to power generation and electrification domains.

The successful candidate will drive innovation in simulation capabilities, enable digital twin development, and collaborate with cross-disciplinary teams to