PhD Candidate in Deep Learning for Acoustic Monitoring

WorkplaceZurich, Zurich region, Switzerland


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100%, Zurich, fixed-term

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.

Project background

In recent years, the popularity of acoustic monitoring has grown rapidly thanks to recent advances in acoustic sensor technology, which are now cheap, non-invasive and easy to install. Thereby, genuine interest from the industry is emerging since the sound emitted by a machine during operation can be indicative of the process quality and of the machine health. In addition, acoustic monitoring has several other applications, like event detection for multimedia system or wildlife surveying for ecological and behavioral studies.

However, the monitoring task from audio recordings stays complex because it consists to analyze huge, noisy high-frequency signals. It is difficult to build a monitoring system that automatically detects relevant features in the recordings and that is also robust to several operating conditions.

For this project, we aim to use recent advances in artificial intelligence and deep learning to overcome these limitations.

Job description

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,...)

Your profile

We are looking for a PhD candidate with a strong analytical background and an outstanding Msc degree in Engineering, Computer Science, Physics, Applied Mathematics, or a related field. The candidate should have a proven experience in deep learning and in the application of deep learning to solve a real-world problem. The candidate should have good programming skills in Python, in particular Tensorflow or Pytorch and have knowledge in signal processing (Wavelet/spectral analysis, time series processing). Experience in audio signal processing is an advantage but is not a prerequisite. Professional command of English (both written and spoken) is mandatory. German is an advantage. We expect the candidate to be self-driven with strong problem solving abilities and out-of-the-box thinking. The duration of the PhD position is foreseen for three years.

ETH Zurich

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.
Working, teaching and research at ETH Zurich

In your application, please refer to and reference JobID 52613.

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