• Research on machine-learning-based predictive maintenance for axial compressor stages in close coordination with the experimental and signal-processing partners.
• Design of unsupervised/self-supervised anomaly detection models and supervised damage classification models for multi-modal sensor data
• Application of explainable AI techniques to identify diagnostic signatures and inform sensor selection
• 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 (Prof. Peitsch, Prof. Sarradj) at TU Berlin.
• 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 turbomachinery aerodynamics or structural dynamics of rotating machinery
• Theoretic knowledge and demonstrated practical experience in CNN, LSTM, Transformer, and Autoencoder architectures for time-series data
• Practical experience with explainable AI tooling
• Proven, advanced-level Python programming experience in PyTorch or JAX and git-based version control
• Very good English and German language skills in word and writing
• Scientific publishing and presenting desirable
• Ability to work independently, communication skills, ability to work in a team; intercultural skills desirable

• 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.