Research Computational Scientist — Mesh Generation & CFD

Oak Ridge National Laboratory; Oak Ridge (TN); On-site; USD 90,000 - 140,000; Full time

Benefits offered by this job

Relocation assistance

Educational assistance

Flexible work hours

On-site fitness facilities

Health care and retirement plans

Job summary

Oak Ridge National Laboratory seeks a Research Computational Scientist to advance CFD mesh generation and discretization for large-scale simulations. You will collaborate with SMEs to refine methods and implement them into automated workflows with emphasis on unstructured discretization and boundary-layer meshing.

The role aims to push adaptive meshing, design/shape optimization, and solution optimization while engaging with a broad community in industry and academia to advance the

Qualifications

• MS or PhD in mathematics, computer science, engineering with relevant experience.
• Background in CFD/mesh generation and scalable algorithms for exascale HPC.

Responsibilities

• Understand/refine CFD applications and discretization techniques with SME input.
• Lead implementation into large-scale automatic computational work-flows focusing on unstructured CFD and conformal mesh generation.
• Engage with industry, national labs, and academia to advance CFD state-of-the-art.

Skills

CFD mesh generation

CFD discretization

Parallel computing

HPC in Linux

Surrogate modeling

Data analytics

C++

Python

GPU programming

Excellent communication

Education

MS in mathematics, computer science, engineering or PhD in related field with relevant experience

Tools

C++

Python

GPU programming

Linux HPC

Job description

Oak Ridge National Laboratory seeks a Research Computational Scientist to advance CFD mesh generation and discretization for large-scale simulations. You will collaborate with SMEs to refine methods and implement them into automated workflows with emphasis on unstructured discretization and boundary-layer meshing.

The role aims to push adaptive meshing, design/shape optimization, and solution optimization while engaging with a broad community in industry and academia to advance the