Project Goals
This project aims to develop fast, flexible, and physics-based reduced order models (ROMs) and multi-scale closures that deliver both precision and robust uncertainty quantification. We will integrate cutting-edge machine learning calibration workflows that enable the concurrent optimization of interconnected processes. The focus is on real-world applications in the battery sector, particularly on electrode production and recycling.
Place of Employment
Institute of Process and Particle Engineering, TU Graz, Inffeldgasse 13, 8010 Graz
Supervisory Team
Stefan Radl (TU Graz; primary), Christoph Spijker (MU Leoben), Katherine Mazzio (TU Wien)
Secondments
1 month secondment at industrial partner (Graz), multiple short visits
Admission Requirements
Master degree in chemical engineering, physics, or similar study program
Essential Qualifications
Experience in the field of particle flow modeling (especially calibration of flow models, and the development of experimental devices for particle and powder flow characterization), as well as programming experience in Python. Experience with software development tools (e.g., git) is a plus.
Offered Employment
Full time (40h/week) for 48 months
