PhD position: Machine learning for analysis and control of complex fluid flows


In this project we will combine and further develop the scientific and technological know-how of TU/e and SISSA research groups towards the use of Machine Learning techniques. The aim of the project is to develop tools that will allow a deeper understanding, modeling and control capabilities for turbulent flows.

The PhD candidate will develop innovative tools for the analysis, modeling, reduction and control of chaotic flows, aimed at answering the following questions: 

  • Can we advance our understanding of turbulence phenomenology combining state-of-the-art statistical tools and reverse engineering of self-discovered machine learning tools?
  • Can we improve, through supervised and unsupervised approaches, current models for turbulence increasing fidelity and reducing computational costs?
  • Can we, using reinforcement learning, better understand the fundamental physics of the energy cascade in turbulent flows?
  • Can we  have better computational performances by exploring Model Reduction techniques?
  • Can we  increase complexity thanks to efficient model order reduction?


For more info, please visit the project website.