In the Magister project there are 15 vacancies for Postdocs. Below you'll find a overview with the different vacancies, partners and subjects. Once available, a link to the specific vacancy and how to apply will appear.
We are in the process of hiring, but we still have one position available!
- Data-driven machine learning with Gaussian Processes to eliminate thermoacoustic instability (University of Cambridge)
- Supervised learning algorithms for distributed parameter models of thermoacoustic oscillations (AMINES)
- Combustion Data Science and Analytics. (GE Deutschland)
- Deep Learning approach to enable combustion/acoustic coupling (ANSYS)
- LES of spray combustion for low order modelling of dynamics: Uncertainty Quantification. (Technische Universität München)
- Numerical study of thermo-acoustic instabilities in spray flames. (CERFACS)
- LES of compressible turbulent flow through combustor liner and dilution holes (University of Twente)
- Physics-based machine learning in thermoacoustics, from lab to engine (University of Cambridge)
- Characterization and modelling of acoustically absorbing liners. (Technische Universität München)
- LES of Acoustically forced spray flames, developing open source code SU2 with liquid fuel combustion. (University of Twente)
- Determination of acoustic response of kerosene spray flames at atmospheric pressure and preheated air supply. (Karlsruhe Insitute of Technology)
- Characterization of acoustically (un)forced kerosene spray flames at elevated pressure and preheated air. (University of Twente)
- Determination of combustion dynamics sub-models: machine learning based on scale resolving simulations. (GE Deutschland)
- LES of spray combustion using machine learning enhanced spray models. (SAFRAN Tech)
- Numerical study of thermo-acoustic instabilities in a helicopter engine combustor (Safran Helicopter Engines)
For more background information on the different vacancies, download the pdf.