Ph.D. Position
Your tasks include
- Push forward the development of data-driven LES closures
- Generate consistentmodels for high order DG schemes
- Manage various PhD research activities in this field
- Present your work at conferences and workshops Requirements
Requirements
- Ph.D. degree or equivalent in engineering, appled math or related fields
- Strong background in at least one of the following: High order numerical methods for PDEs, machine learning and Large Eddy Simulation
- Experience in Reinforcement Learning and/or DG methods is a definite plus
- Experience in research software design, development and management
- Fluency in either German or English (written & oral)
- Please read https://doi.org/10.1063/5.0176223 and https://doi.org/10.1016/j.ijheatfluidflow.2022.109094. If this excites you, consider applying!
The IAG encourages publication of results in scientific journals and supports participation in international conferences. The position is initially limited to two years, but an extension beyond this period is possible.
The IAG is committed to increasing the number of female scientists. Severely disabled persons are given priority if equally qualified.
Please send your full application (cover letter, CV, transcripts) or questions Exclusively to
applications.nrg@iag.uni-stuttgart.de.
Please mention PostDoc: LES in the subject!
| Kontakt | Prof. Dr.-Ing. Andrea Beck |
|---|---|
| Datei-Anhänge |