Post-Doctoral Fellow AF7770

Oklahoma State University’s School of Mechanical and Aerospace Engineering seeks a Post-Doctoral Fellow to join Dr. Chitrarth Prasad’s Flow Physics Simulation Laboratory (FPSL). The role centers on fundamental research into data-driven discovery of vortex-dominated flows, integrating experimental measurements, high-fidelity numerical simulations, and interpretable machine-learning methods to derive reduced-order descriptions and governing relationships for leading edge vortices (LEVs).

Responsibilities

• Develop interpretable, physics-informed data-driven 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 motions, geometries, Reynolds numbers, and flow regimes.
• Develop well-documented research software and reproducible data-analysis workflows.
• Prepare and submit peer-reviewed journal articles, conference papers, and technical presentations.
• Present research findings at conferences, workshops, and scientific meetings.
• Mentor graduate and undergraduate students 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.
• Contribute to new fundamental research directions, research proposals, and scientific publications.
• Participate in peer review and relevant professional societies.
• Represent the research group at conferences and professional meetings.

Requirements

• 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.

Benefits

• Comprehensive medical plans.

More information on benefits is available at https://hr.okstate.edu/benefits/index.html.