PhD student in Autonomous Velocimetry for Fluid Mechanics

Materials science and technology are our passion. With our cutting-edge research Empas around 1100 employees make essential contributions to the well-being of society for a future worth living. Empa is a research institution of the ETH Domain. The Laboratory for Computational Engineering in Dübendorf is offering a position for two motivated doctoral students. Your tasks 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 data. 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. Our offer We offer a stimulating multidisciplinary research environment within the ETH Domain with close collaboration between Empa, ETH Zürich, and other international research partners. Empa provides state‑of‑the‑art experimental and computational infrastructure, internationally competitive employment conditions, and strong support for personal and professional development. The PhD student will be enrolled in the ETH Zürich / University of Zürich doctoral program depending on academic affiliation. The position is available immediately or upon agreement. For further information about the position please contact: Dr Claudio Mucignat, Scientist and Principal Investigator, or Dr Ivan Lunati, Head of Laboratory for Computational Engineering. We look forward to receiving your complete online application including a letter of motivation, CV, certificates, diplomas, and contact details of two reference persons. Please submit these exclusively via our job portal. Applications by e‑mail and by post will not be considered. Patricia NitzscheStv. Leiterin Human Resources / Dep. Head Human Resources #J-18808-Ljbffr