Research Associate in Mechanical and Aerospace Engineering

University of Virginia; Charlottesville (VA); On-site; USD 53,000 - 58,000; Full time

Job summary

The Autonomous Flow Interaction (AFI) Laboratory in the Department of Mechanical and Aerospace Engineering at University of Virginia is seeking a Postdoctoral Research Associate to advance estimation and control of aero- and hydrodynamic flows for autonomous flight and underwater propulsion.

The successful candidate will develop and implement efficient numerical methods for flow modeling, estimation, and control, contributing to both model-based and model-free adaptive approaches, within a

Qualifications

• PhD required by start date; within 5 years post-PhD.
• Research experience in AI/ML for fluid dynamics, reduced-order modeling, or flow control.
• Experience with numerical methods for flow modeling and estimation is a plus.

Responsibilities

• Develop, implement, and analyze efficient numerical methods for flow modeling, estimation, and control.
• Work as an algorithm developer within the AFI Lab on model-based and model-free adaptive approaches.

Skills

AI/ML for fluid dynamics

Low-order modeling

Linear Robust Control

Nonlinear Adaptive Control

Experimental flow measurement

Education

PhD in Mechanical or Aerospace Engineering, Mathematics or related field

Job description

The Autonomous Flow Interaction (AFI) Laboratory in the Department of Mechanical and Aerospace Engineering at University of Virginia is seeking a qualified candidate for a Postdoctoral Research Associate position.

Research in the laboratory focuses on estimation and control of aero‑ and hydrodynamic flows for applications in autonomous flight and underwater propulsion. The successful candidate will contribute to low‑order modeling of complex fluid flows and development of advanced algorithms for estimation and control, exploring both model‑based and model‑free, adaptive approaches.

The Postdoctoral Research Associate will work as an algorithm developer within the AFI Lab and will be responsible for the development, implementation, and analysis of efficient numerical methods for flow modeling, flow estimation, and flow control.

Qualification Requirements

By the start date, applicants must have earned a PhD in Mechanical or Aerospace Engineering, Mathematics or a related field, and be within 5 years post‑PhD. Candidates should demonstrate research experience in a few of the following areas:

• AI/ML for the fluid dynamics
• Low‑order and/or reduced‑order flow modeling
• Linear and Robust Control
• Nonlinear or Adaptive Control
• Experimental flow measurement methods

Preferred Qualifications

Preference will be given to candidates with:

• Demonstrated ability to manage multiple projects and work well in a team environment
• Peer‑reviewed publications in fluid dynamics, estimation and control theory, or flight vehicle control

Salary

The anticipated hiring salary is $53,000 - $58,000 commensurate with experience.

Application Procedure

Apply online through the UVA Job Board and search for position number R0085016.

Applicants Should Upload

• A cover letter
• Curriculum vitae
• Contact information for three professional references

Application Deadline

Review of applications will begin on July 20, 2026; however, the position will remain open until filled. The University will perform background checks on all new hires prior to employment.

This is a one‑year appointment. Renewal is contingent upon available funding and satisfactory performance.

Questions regarding this position should be directed to Professor Frank Lagor at [e-mail].

For questions about the application process, please contact Richard Haverstrom, Academic Recruiter, at [e-mail].

For more information on the benefits available to a Research Associate at UVA, visit hr.virginia.edu/benefits.

The University of Virginia is an equal opportunity employer. All interested persons are encouraged to apply, including veterans and individuals with disabilities. Learn more about UVA’s commitment to non‑discrimination and equal opportunity employment.