Scientific Research Assistant: Deep learning for Predictive Maintenance Applications in Complex Industrial Systems
The Chair of Intelligent Maintenance Systems at the Department of Civil, Environmental and Geomatic Engineering at ETH Zurich focuses on developing algorithms and decision support systems for data-driven intelligent maintenance of industrial assets.
The amount of measured and collected condition monitoring data for complex industrial assets has been recently increasing significantly due to falling costs, improved technology, and increased reliability of sensors and data transmission. The measured condition monitoring signals of complex industrial assets are typically high dimensional, highly redundant, have several interdependencies and prevalent non-linear relationships. The diversity of the fault types and operating conditions makes it often impossible to extract and learn the fault patterns of all the possible fault types affecting a system and to develop model-based approaches. Even collecting a representative dataset with all possible operating conditions can be a challenging task (depending on the variability of the operating regimes of the assets) and may delay the implementation of data-driven fault detection systems.
Your tasks will consist in working on research projects in the field of data-driven predictive maintenance, partly in collaboration with industrial project partners.
Under the supervision of the project responsible researcher, you will implement different deep learning approaches in Python, using TensorFlow or PyTorch.
In addition, you will be also expected to be proactive and keep up to date with the state of research, suggest promising solutions, perform the implementation and the experiment design, write publications and present your research results at workshops and conferences.
You will work in an interactive international environment on innovative research projects in collaboration with other team members, referring continuously to practical problems and solutions. The research projects will provide you with a unique opportunity to develop a strong interdisciplinary portfolio both in machine learning and in predictive maintenance applications.
The position starts as soon as possible and the project duration is planned for one year with the possibility to prolong. No doctoral studies are foreseen for this position.
We are looking for a highly motivated candidate with the following profile:
- a Master’s Degree in Engineering, physics, applied mathematics, control, computer science or related fields
- Programming experience, preferably in Python, is expected; knowledge in deep learning libraries (Keras, Tensorflow) is beneficial
- Experience in machine and deep learning is mandatory
- Strong analytical skills, proactive, self-driven with strong problem solving abilities and out-of-the-box thinking
- Professional command of English (both written and spoken) is mandatory and knowledge in German is beneficial
We look forward to receiving your online application including a letter of motivation, CV, a brief statement of research interests (1 page), transcripts of all obtained degrees (in English), one publication (e.g. thesis or conference/journal publication: the one that you consider being most relevant to the position that you are applying for), and two reference letters. Only complete applications containing all the required documents will be considered. Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
For more information about the chair please visit: https://ims.ibi.ethz.ch/. Questions regarding the position should be directed to Dr. Gabriel Michau by email michau[at] ibi.baug.ethz.ch (no applications).