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Texas A&M University

Postdoctoral Researcher in Computational Fluid Dynamics and Turbulence Texas A&M University in United States

Degree Level

Postdoc

Field of study

Mechanical Engineering

Funding

Available

Country flag

Country

United States

University

Texas A&M University

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Keywords

Mechanical Engineering
Aerospace Engineering
Fluid Mechanics
Computational Science
Turbulence
High Performance Computing
Boundary Layer Theory
Data-driven Modeling
Wall-bounded Flows
Particle Tracking
Physics

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About this position

Texas A&M University is recruiting a postdoctoral researcher in the Department of Aerospace Engineering to work on computational fluid dynamics and turbulence. The project focuses on high-fidelity simulations of wall-bounded flows, including pressure-gradient boundary layers, separation, relaminarization, wakes, and airfoils.

The successful researcher will help develop advanced computational tools and analysis methods for complex flow phenomena. The role combines simulation, method development, and data analysis to improve understanding of turbulence and unsteady flow behavior.

The postdoc will work with a multidisciplinary, multi-university research team and contribute to physics-based and data-driven modeling efforts. The position also includes collaboration with faculty, students, and external research partners, as well as opportunities to produce technical reports, conference presentations, and peer-reviewed publications.

The research environment is centered in the Texas A&M Engineering ecosystem, with access to high-performance computing resources and a department that emphasizes both fundamental and applied aerospace research.

Funding details

Available

What's required

Doctorate degree is required. A Ph.D. in Aerospace Engineering, Mechanical Engineering, Applied Mathematics, Physics, or a related field is preferred. Strong written and verbal communication skills are required, along with a strong background in fluid dynamics, turbulence, CFD, and scientific computing. Helpful experience includes high-fidelity simulation methods such as DNS and/or spectral or high-order numerical methods, use of high-performance computing systems, Lagrangian particle tracking and Eulerian-Lagrangian data analysis, wall-bounded turbulence, pressure-gradient flows, flow separation, wake dynamics, and familiarity with reduced-order modeling, surrogate modeling, or physics-informed machine learning.

How to apply

Prepare a resume/CV, three work references with contact information, and a few representative publications. Submit the application through the Workday posting and ensure all required job application data is entered. Provide complete materials to avoid an invalid submission.

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