Summary
The Erinin Research Group and the VinuesaLab at the University of Michigan are seeking a Postdoctoral Research Fellow for a collaborative project investigating flow and acoustic control over wings. This interdisciplinary role combines cutting-edge deep reinforcement learning (DRL), experimental fluid mechanics, flow control, and adaptive metamaterials. The fellow will design and conduct wind-tunnel experiments that couple real-time particle image velocimetry (PIV) with responsive surface materials, generating crucial experimental data to train and validate learning-based control strategies.
Postdoctoral Research Fellow in Real-Time Flow Control, University of Michigan, USA
Job Details Overview
| Category | Details |
|---|---|
| Title | Real-Time Flow Control Using Adaptive Metamaterials |
| Designation | Postdoctoral Research Fellow |
| Location | Department of Aerospace Engineering and Department of Mechanical Engineering, University of Michigan, Ann Arbor |
| Research Area | Experimental Fluid Mechanics, Flow Control, Data-driven Control (DRL), Adaptive Metamaterials |
| Last Date to Apply | Open Now |
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Job Description
The postdoctoral fellow will work at the interface of experimental fluid mechanics, data-driven control, and materials engineering, with collaborators in the US and Europe. Core responsibilities include:
- Leading wind-tunnel testing of the strain-programmable metamaterial wing-tip module.
- Continuing the development and operation of a real-time PIV control framework designed specifically for DRL control.
- Characterizing tip-vortex dynamics, drag, and acoustic signatures.
- Supporting the training and deployment of DRL-based control policies used in the experiments.
- Mentoring a PhD student and coordinating activities across both research groups.
- Co-authoring publications.
Eligibility & Qualifications
- Ph.D. in engineering or a related STEM field.
- Experience in experimental fluid mechanics.
- Experience with imaging techniques like particle image velocimetry (PIV).
- Experience designing experiments and working in wind tunnels or similar experimental facilities.
- Programming skills in MATLAB or Python; real-time data acquisition experience is a plus.
- Desired: Interest in machine learning-guided experimental control.
How to Apply
Interested candidates should send their Curriculum Vitae (C.V.) and a short research statement directly to the principal investigators:
- Ricardo Vinuesa: rvinuesa@umich.edu
- Martin Erinin: merinin@umich.edu
U-M Aero Faculty Lecture: Ricardo Vinuesa on Data-driven reduced-complexity modeling of fluid flows This video features a lecture from Ricardo Vinuesa at the University of Michigan discussing the application of machine learning methods to fluid mechanics, which directly aligns with the research focus of this postdoctoral position.
