Postdoc: Data-Driven Flow Physics & Vortex Modeling

Oklahoma State University; Stillwater (OK); On-site; USD 48,000 - 70,000; Full time

Job summary

Oklahoma State University’s School of Mechanical and Aerospace Engineering invites applications for a Post-Doctoral Fellow position to join the Flow Physics Simulation Laboratory (FPSL).

The successful candidate will conduct fundamental research on data-driven discovery of vortex-dominated flows using experimental measurements, high-fidelity simulations, and interpretable machine-learning methods to identify reduced-order descriptions and governing relationships for leading edge vortices (LEVs).

Qualifications

• PhD in a related engineering or mathematical field.
• Experience with data-driven and machine learning methods.
• Strong programming skills in scientific languages (Python/MATLAB/C/C++/Fortran).
• Evidence of scholarly research through publications or conference papers.

Responsibilities

• Develop interpretable data-driven and physics-informed models for LEV state prediction from experimental measurements.
• Apply sparse identification, machine learning, and reduced-order modeling to datasets.
• Prepare peer-reviewed journal articles and conference presentations.
• Mentor graduate and undergraduate students and contribute to reproducible data-analysis workflows.

Skills

Fluid mechanics

Computational fluid dynamics

Data-driven modeling

Machine learning

System identification

Reduced-order modeling

Education

PhD in Mechanical Engineering, Aerospace Engineering, or Applied Mathematics

Tools

Python

MATLAB

C++

Fortran

Job description

Oklahoma State University’s School of Mechanical and Aerospace Engineering invites applications for a Post-Doctoral Fellow position to join the Flow Physics Simulation Laboratory (FPSL).

The successful candidate will conduct fundamental research on data-driven discovery of vortex-dominated flows using experimental measurements, high-fidelity simulations, and interpretable machine-learning methods to identify reduced-order descriptions and governing relationships for leading edge vortices (LEVs).