Stellenticket Technische Hochschule Brandenburg

• Research in nonlinear friction-affected structural dynamics utilizing machine learning methods (ML) in turbomachinery for the BMWE-funded project “Predictive Friction Damping by Topography Resolving Simulation and Texture Design through AI (ENDORSE)”
• Development of multi-scale simulation methods and AI-based topography generators
• Implementation of scientific open-source program code with high-quality standards
• Publication of scientific results in international journals and at scientific conferences
• Collaboration with project partners at the University of Stuttgart
• Supervision of student theses within the project scope
• Science communication for non-university audiences

• A successfully completed scientific university degree (Master, Diplom or equivalent) in Engineering Sciences, applied mathematics or computer science
• In-depth knowledge of physics-informed AI methods in the field of turbomachinery
• Proven knowledge and practical experience in the application of deep learning methods to three-dimensional spaces
• Demonstrated programming experience in Python, PyTorch and NVIDIA PhysicsNeMo
• Very good English language skills
• Very good command of the German language
• Scientific publishing and presenting desirable
• Ability to work independently, communication skills, ability to work in a team, intercultural skills are a plus

• An inclusive and appreciative working atmosphere in a committed and diverse team
• The compatibility of family and career through flexible working time models incl. home office

Please send your application with the reference number and the usual documents by E-Mail to Prof. Dr. Merten Stender via bewerbungen@cpsme.tu-berlin.de.

By submitting your application via email you consent to having your data electronically processed and saved. Please note that we do not provide a guarantee for the protection of your personal data when submitted as unprotected file. Please find our data protection notice acc. DSGVO (General Data Protection Regulation) at the TU staff department homepage: https://www.abt2-t.tu-berlin.de/menue/themen_a_z/datenschutzerklaerung.

To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities. Applications from people of all nationalities and with a migration background are very welcome.