Postdoctoral Associate

Job Description

The Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech is seeking a postdoctoral researcher to work on two projects. The first project (50% effort), with Prof. Chris Roy as the primary collaborator, involves applying machine learning tools to improve turbulence models for computational fluid dynamics (CFD) predictions of hypersonic flows. This work is in collaboration with Spectral Sciences, Inc. and is funded by the US Navy (ONR and NAWCAD). This project will involve research in Favre-Averaged Navier-Stokes turbulence models, aided by physics-constrained machine learning, to predict surface heating, skin friction, and flow separation for hypersonic flows. The second project (50% effort), with Prof. Todd Lowe as the primary collaborator, involves supporting ongoing and future CFD model validation and development experiments at the Virginia Tech Advanced Propulsion and Power Laboratory including engine inlet distortion (ONR, AFRL, and industry sponsors) and engine particle transport (ONR and industry sponsors).

Additional information on Dr. Roy’s and Dr. Lowe’s research can be found at:

www.aoe.vt.edu/people/faculty/roy.html (Dr. Roy)

www.aoe.vt.edu/people/faculty/lowe.html (Dr. Lowe)

The position is at the university’s main campus in Blacksburg, VA. Opportunities for teaching and service-related activities are available to successful candidates. Chosen candidate must have strong communication skills and have ability to work effectively in a team dynamic.

Required Qualifications

-Ph.D. in Aerospace Engineering, Mechanical Engineering, or related discipline. PhD must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining. -Experience with conducting research in computational fluid dynamics (CFD) -Experience with Reynolds- and/or Favre-averaged Navier-Stokes turbulence models -Experience programming in modern Fortran, C, C++, or Python -US citizenship or US permanent residency, due to sensitivity of work being performed

Preferred Qualifications

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

Overtime Status

Exempt: Not eligible for overtime

Appointment Type

Restricted

Salary Information

63,480-65,000; commensurate with experience

Hours per week

40

Review Date

01/29/2027

Additional Information

This position is restricted to US citizenship or US permanent residency due to sensitivity of work being performed.

The successful candidate will be required to have a criminal conviction check.

About Virginia Tech

Dedicated to its motto, Ut Prosim (That I May Serve), Virginia Tech pushes the boundaries of knowledge by taking a hands-on, transdisciplinary approach to preparing scholars to be leaders and problem-solvers. A comprehensive land-grant institution that enhances the quality of life in Virginia and throughout the world, Virginia Tech is an inclusive community dedicated to knowledge, discovery, and creativity. The university offers more than 280 majors to a diverse enrollment of more than 36,000 undergraduate, graduate, and professional students in eight undergraduate colleges, a school of medicine, a veterinary medicine college, Graduate School, and Honors College. The university has a significant presence across Virginia, including Blacksburg, the greater Washington, D.C. area, the Health Sciences and Technology Campus in Roanoke, sites in Newport News and Richmond, and numerous Extension offices and research institutes. A leading global research institution, Virginia Tech conducts more than $650 million in research annually.

Virginia Tech endorses and encourages participation in professional development opportunities and university shared governance. These valuable contributions to university shared governance provide important representation and perspective, along with opportunities for unique and impactful professional development.

Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law.

If you are an individual with a disability and desire an accommodation, please contact Dr. Chris Roy at cjroy@vt.edu during regular business hours at least 10 business days prior to the event.