Postdoctoral Associate

Virginia Tech • Blacksburg, VA

4d • Onsite

About The Position

The Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech seeks a postdoctoral researcher to work on two projects at the university’s main campus in Blacksburg, Virginia. The role is split evenly between the projects.

The first project, in collaboration with Spectral Sciences, Inc. and funded by the US Navy (ONR and NAWCAD), applies machine learning tools to improve turbulence models for computational fluid dynamics (CFD) predictions of hypersonic flows. Research will involve Favre-Averaged Navier-Stokes turbulence models aided by physics-constrained machine learning to predict surface heating, skin friction, and flow separation. Prof. Chris Roy is the primary collaborator.

The second project supports ongoing and future CFD model validation and development experiments at the Virginia Tech Advanced Propulsion and Power Laboratory. These include engine inlet distortion and engine particle transport studies, sponsored by ONR, AFRL, and industry partners. Prof. Todd Lowe is the primary collaborator. Opportunities for teaching and service-related activities are available. Candidates must have strong communication skills and be able to work effectively in a team dynamic. The position is restricted to US citizens or US permanent residents due to the sensitivity of the work. The successful candidate will be required to complete a criminal conviction check.

Requirements

• Ph.D. in Aerospace Engineering, Mechanical Engineering, or a related discipline.
• The Ph.D. must have been awarded no more than four years before the effective appointment date, with at least one year of eligibility remaining.
• Experience conducting research in computational fluid dynamics (CFD).
• Experience with Reynolds- and/or Favre-averaged Navier-Stokes turbulence models.
• Programming experience in modern Fortran, C, C++, or Python.
• US citizenship or US permanent residency due to the sensitivity of the work.
• Strong communication skills and ability to work effectively in a team dynamic.

Nice To Haves

• Experience with machine learning techniques.
• Experience with experimental fluid dynamics techniques.
• Experience with high-speed compressible flows.
• Experience with high-performance computing.

Responsibilities

• Apply machine learning tools to improve turbulence models for CFD predictions of hypersonic flows.
• Conduct research using Favre-Averaged Navier-Stokes turbulence models aided by physics-constrained machine learning to predict surface heating, skin friction, and flow separation.
• Support ongoing and future CFD model validation and development experiments at the Virginia Tech Advanced Propulsion and Power Laboratory.
• Support experiments involving engine inlet distortion and engine particle transport.
• Potentially participate in teaching and service-related activities.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree