Research Assistant Position: Development of Rail Roughness Measuring Technique and Big Data Analysis
The Institute of Machine Tools and Manufacturing (IWF) under the Department of Mechanical and Process Engineering (D-MAVT) is an international leading research group on machine tools and in the field of production engineering. Recent topics of research focus on novel technologies such as additive manufacturing, Artificial Intelligence (AI), Machine Learning (ML), Big Data analysis and Industry 4.0.
The increasing velocity of trains, now reaching up to 250 km/h, creates a significant amount of noise. It is mainly generated by the aerodynamics and the rail-wheel interaction. Due to this interaction, the so-called rolling noise is emitted, contributing significantly to the overall acoustic pollution. The results of sonRAIL study showed direct correlation between the noise development and the surface quality of rail and wheel, thereby creating the willingness to precisely measure this quantity. Unfortunately the existing methods do not allow for a fast and precise evaluation of the rail roughness, making it impossible to keep track of the rail surface status. The high velocity of the train is just one challenge among many that such a measuring device shall overcome. For instance, another major problem would be the data processing. Artificial Intelligence will possibly be used for feature extraction out of the huge amount of data. State of the art dry grinding technology of railways induces increasing noise after the grinding process. The noise only decreases after some train overrun, which smooths the surface of the rails. A closed loop system with integrated Machine Learning for the detection of optimal process parameters for railway grinding needs to be developed, in order to improve the process itself as well as the general track maintenance.
For this industry related research project, we are looking for a research assistant who can support the project. You will be working on the development of methodology and algorithms to accurately measure surface roughness at high speed using contactless sensors. Consequently, the developed product will need to be tested and evaluated in the lab, as well as in the real world, on running trains. Theoretical consideration and modeling will then be combined with experimental tasks. In a second step, the candidate will need to establish a method to deal with such an enormous amount of data and make it usable for the clients. This will open doors for diving into Machine Learning and Big Data analysis, also related to the predictive maintenance of the rail network or to the grinding process optimization.
We are looking for a highly motivated, self-organized and eager to learn candidates holding a Master's degree in Engineering or Physics. Qualified candidates should have the following capabilities:
- Good knowledge of data analysis and signal processing
- Interest in metrology and measuring techniques
- Programming experience, preferably in Python and LabView
- Competences in Information Technology
- Languages: fluency in German and English
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