PhD Student in Deep Learning for Acoustic Monitoring
The Chair of Intelligent Maintenance Systems focuses on developing intelligent algorithms to improve performance, reliability and availability of complex industrial assets and making the maintenance more cost efficient. Our research focuses on deep learning, domain adaptation, hybrid approaches (combing physical performance models and deep learning algorithms), and deep reinforcement learning. The data we are typically dealing with comprises heterogeneous multivariate time series data of different types, with different sampling rates and different degrees of uncertainties.
The successful applicant will drive the research in the field of deep learning applied to time series data from audio recordings. The position includes following responsibilities:
- Research and development of innovative solutions for sound processing in the context of audio monitoring
- Collaboration with the industry for data collection, data pre-processing, development of the solutions and their knowledge transfer to the application field.
- Supervision of master students
- Limited teaching responsibilities
- Involvement in academic activities (e.g., conference, seminar organisation,…)
Switzerland › Zurich
02 June 2021