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Optimizing vehicle aerodynamics to reduce transportation emissions, understanding airborne disease transmission, and predicting climate-related transport phenomena all require precise knowledge of fluid flow dynamics. Advanced experimental methods such as Particle Image Velocimetry (PIV) and 3D Lagrangian Particle Tracking (LPT) provide crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics, focusing on: Designing multi-fidelity neural networks for adaptive flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system that autonomously optimizes 3D velocimetry measurements by dynamically adjusting camera positions and optical parameters. Integrating the framework within a digital twin environment for pre-training and simulation-based optimization, enabling autonomous measurement campaigns and real-time data assimilation.This research combines fluid mechanics, artificial intelligence, and robotics to establish the foundation for the next generation of autonomous experimental diagnostics in complex flow environments. Your profile
We are looking for 2 highly motivated PhD students with a strong analytical background and an MSc degree in Mechanical or Aerospace Engineering, Physics, Computational Science, or a related discipline. The candidates should have:
* Solid programming skills (Python, MATLAB, or C++).
* Knowledge of the OpenCV library.
* Strong interest in machine learning, reinforcement learning, and fluid dynamics.
* Ability to work independently and collaboratively in an interdisciplinary team.
* Excellent command of English, both written and spoken.
* Experience with experimental fluid mechanics and computer vision is an advantage.
We offer a