Post-Doctoral Fellow AF7770

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

Post-Doctoral Fellow for Data-Driven Discovery of Vortex-dominated Flows

Position Summary

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

The successful candidate will primarily conduct fundamental research on data-driven discovery of vortex-dominated flows. The project combines experimental measurements, high-fidelity numerical simulations, and interpretable machine-learning methods to identify reduced-order descriptions and governing relationships for leading edge vortices (LEVs). Relevant approaches may include sparse system identification, SINDy, graph-based learning, and other machine-learning methods for fluid mechanics.

Although the position will primarily support this project, the candidate will have opportunities to contribute to other fundamental research conducted within FPSL. Current research spans incompressible low-speed flows, compressible and high-speed flows, unsteady aerodynamics, aeroacoustics, and multiphase flows. The candidate will also mentor graduate and undergraduate students, contribute to peer-reviewed publications and conference presentations, and help develop new research directions within the laboratory.

Responsibilities And Duties

Research (80%)

• Develop interpretable data-driven and physics-informed models for LEV state prediction from experimental measurements.
• Apply sparse system identification, machine learning, and reduced-order modeling to experimental and computational datasets.
• Evaluate model accuracy, robustness, uncertainty, interpretability, and generalizability across different motions, geometries, Reynolds numbers, and flow regimes.
• Develop well-documented research software and reproducible data-analysis workflows.
• Prepare peer-reviewed journal articles, conference papers, and technical presentations.
• Present research findings at conferences, workshops, and scientific meetings.

Student Mentoring (15%)

• Mentor graduate and undergraduate students working on computational and data-driven fluid-mechanics projects.
• Guide students in interpreting results and preparing publications, presentations, and technical reports.
• Support collaborative research among students working on low-speed, high-speed, and multiphase-flow problems.

Research Development and Professional Activities (5%)

• Contribute to the development of new fundamental research directions.
• Assist with research proposals and scientific publications.
• Participate in peer review and relevant professional societies.
• Represent the research group at conferences and professional meetings.

Required Qualifications

• Ph.D. in Mechanical Engineering, Aerospace Engineering, Applied Mathematics, or a closely related field.
• Strong background in fluid mechanics, computational fluid dynamics, data-driven modeling, or a related area.
• Experience applying machine learning, system identification, reduced-order modeling, or advanced data-analysis methods to scientific or engineering problems.
• Proficiency in scientific programming using Python, MATLAB, C++, Fortran, or comparable languages.
• Evidence of scholarly research through peer-reviewed journal publications, conference papers, or equivalent research products.
• Ability to work collaboratively with faculty, graduate students, undergraduate students, and researchers from other institutions.

Preferred Qualifications

• Experience applying machine learning specifically to fluid-mechanics problems.
• Experience with sparse system identification, SINDy, physics-informed machine learning, graph neural networks, autoencoders, or nonlinear reduced-order modeling.
• Experience with high-performance computing and parallel data-processing workflows.

Salary And Benefits

Salary will be commensurate with education, experience, qualifications and contingent on available funding. Benefits include comprehensive medical plans. Information on benefits can be found at https://hr.okstate.edu/benefits/index.html

Special Instructions To Applicants

The process of reviewing applications will begin soon and will continue until a successful candidate is selected.

Questions about the position should directed to Dr. Chitrarth Prasad at [e-mail].