PhD position in Big Data Analysis using Artificial Intelligence in Railway Measurement and Grinding
ETH Zurich Department of Computer Science, Mathematics and Engineering
Switzerland

JOB DETAILS
Title PhD position – Big Data Analysis using Artificial Intelligence in Railway Measurement and Grinding
Employer ETH Zurich
Job location Rämistrasse 101, 8006 Zurich
Published August 7, 2019
Application deadline Unspecified
Job types PhD
Fields Transportation Engineering, Artificial Intelligence, Computer Communications (Networks), Computing in Mathematics, Natural Science, Engineering and Medicine, Railway Engineering, Big Data


The Institute of Machine Tools and Manufacturing (IWF) at the Department of Mechanical and Process Engineering performs international leading research on machine tools and in the field of production engineering. A further research focus is on novel topics such as additive manufacturing, Artificial Intelligence (AI), Machine Learning (ML), Big Data Analysis or Industry 4.0. For a new industry related research project, we are looking for a new PhD candidate.

 

Project background
The traveling velocity of Swiss pendolino reaching 250 km/h creates both aerodynamic noise as well as noise from the train wheel – track interaction. The rolling noise contributes 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. The high velocity of the train is the main challenge for in-situ measurements of the surface roughness and waviness. Another obstacle is the data processing. Artificial Intelligence (AI) needs to be used for feature extraction out of the huge amount of data (Big Data). State of the art in 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 (ML) for the detection of optimal process parameters for railway grinding needs to be developed.

 

Job description
The PhD student participating in this research project is expected to develop a closed loop model for rail measurement, assesment and improvement. Through the close cooperation with our project partners, she/he will quickly establish contacts with Swiss industry. In our young teams, he/she will work at the forefront of today's knowledge in manufacturing. We offer a modern working environment in a young and motivated team and an interesting topic in a fast growing field of research. This position is a 100% employment and the place of work is Zurich.

 

Your profile
Requirements: The applicant has to have a master degree in Engineering or Physics. The position is coupled with the possibility of a PhD study at ETH Zurich. Qualified candidates should have the following capabilities:

Good knowledge of data analysis and signal processing
Programming experience, preferably in Python
Competences in Information Technology
The candidate is highly motivated and eager to learn. In addition, he/she likes to combine theoretical considerations and models with experimental tasks, having valuable technical skills.

 

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.
Interested?
We look forward to receiving your online application. Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

For further information, please contact Prof. Dr.-Ing. Konrad Wegener, IWF, ETH (wegener@iwf.mavt.ethz.ch), or Dr. Michal Kuffa, IWF, ETH (kuffa@iwf.mavt.ethz.ch)


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