In the Magister project, 15 ESR's will have started by September 1st, 2018. Below you'll find an overview of the different ESR's. As the project goes on, each ESR will introduce him or herself. 


  1. Data-driven machine learning with Gaussian Processes to eliminate thermoacoustic instability (University of Cambridge)
  2. Supervised learning algorithms for distributed parameter models of thermoacoustic oscillations  (ARMINES)
  3. Combustion Data Science and Analytics.  (GE Deutschland)
  4. Deep Learning approach to enable combustion/acoustic coupling (ANSYS)
  5. LES of spray combustion for low order modelling of dynamics: Uncertainty Quantification. (Technische Universität München)
  6. Numerical study of thermo-acoustic instabilities in spray flames. (CERFACS) 
  7. LES of compressible turbulent flow through combustor liner and dilution holes (University of Twente)
  8. Physics-based machine learning in thermoacoustics, from lab to engine (University of Cambridge)
  9. Characterization and modelling of acoustically absorbing liners. (Technische Universität München)
  10. LES of Acoustically forced spray flames, developing open source code SU2 with liquid fuel combustion. (University of Twente)
  11. Determination of acoustic response of kerosene spray flames at atmospheric pressure and preheated air supply. (Karlsruhe Insitute of Technology)
  12. Characterization of acoustically (un)forced kerosene spray flames at elevated pressure and preheated air. (University of Twente)
  13. Determination of combustion dynamics sub-models: machine learning based on scale resolving simulations. (GE Deutschland)
  14. LES of spray combustion using machine learning enhanced spray models. (SAFRAN Tech)
  15. Numerical study of thermo-acoustic instabilities in a helicopter engine combustor (Safran Helicopter Engines)