Research assistant

• Design and implement deep learning models for detection and classification of NVH sounds from automotive brake systems
• Engineer production grade machine learning code with strict standards on reliability, reproducibility and performance
• Coordinate closely with an industrial partner on requirements, integration and interfaces
• Integrate and optimize AI models for embedded systems and architecture level runtime optimization
• Publication of scientific results in international journals and at specialist conferences

• A successfully completed scientific university degree (Master, Diplom or equivalent) in Applied Mathematics, Computer Science, Engineering Sciences, or Physics
• In-depth and practical computer vision expertise in AI-based object detection
• Advanced expertise in sequential data modeling using CNN, RNN, or transformer based architectures
• Professional collaborative software development experience in PyTorch; working level proficiency in C++
• Experience deploying deep learning models to production or embedded environments is desired
• Very good command of the German language

Please send your application with the reference number and the usual documents by E-Mail to Prof. Dr. Merten Stender via adminstration@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/ or quick access 214041.

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.