Research Associate (f/m/d) - Data Science, Condition Monitoring and Predictive Maintenance for Rolling Element Bearings

  • RWTH Aachen University
  • 52074 Aachen, Germany
  • 05/04/2024
Full time Data Science Business Intelligence Software Engineering DevOps

Job Description

Further information

The recruitment takes place as an employee.
The position is to be filled as soon as possible and is limited to one year.
An extension of twice two years is planned.
The fixed-term employment takes place within the framework of the fixed-term options under the Science Temporary Contract Act.
This is a full time position.
There is an opportunity for a doctorate.
The classification is based on the TV-L.
The position is rated TV-L EG 13.

Our profile

The Institute for Machine Elements and System Development researches the basic structural and tribological behavior of machine elements and depicts this in experimentally validated model descriptions. These model descriptions are used to analyze and design the functional, loss and noise behavior of entire technical systems with a focus on the drive technology of wind turbines and mobile machines. The results are computational and constructive designs of concrete technical solutions including proof of the required system properties on large test benches. Numerous experiences with such model-based solution finding through to the conception of configurable products enable the MSE to research and develop methods of model-based systems engineering as a central element of future industrial product development processes.

Ensuring functional safety throughout the product life cycle while limiting maintenance costs and increasing the service life of machines and systems are modern industrial challenges.

The tribology department of the MSE researches the degradation behavior of machine elements. The focus of the research is on application-oriented basic research. The main topics are the interactions between the lubricant and the surfaces of the lubricated components, the wear behavior of machine elements and the changes in lubricants due to use. Another focus is the condition monitoring and service life prediction of machine elements. Methodologically, these topic complexes are examined through close interaction between experiment, simulation and analysis.

your profile

  • Successfully completed university degree (Master or comparable) in the fields of mechanical engineering, mechatronics, electrical engineering, automation technology, CES, data science, computer science or a comparable field
  • Independent and responsible way of working
  • Willingness to familiarize yourself with new subject areas and carry out both theoretical and experimental work
  • Secure command of German and English, both spoken and written
  • Strong communication and teamwork skills
  • Previous knowledge/interests in the field of machine learning are desirable
  • Previous knowledge in the area of ​​digital signal processing is also desirable
  • Interested in leading and developing an interdisciplinary team

Your tasks

  • Working on research projects in the field of tribology (condition monitoring, data science and predictive maintenance for rolling bearings, experimental validation)
  • Implementation of data analysis methods including machine learning methods
  • Acquisition, implementation and presentation of industrial projects in the field of tribology
  • Presentation of research results at national and international levels
  • If desired, participation in university teaching within the framework of lectures and exercises (tribology)
  • Participation in the further development of the tribology department in coordination with the department management

About Us

RWTH is certified as a family-friendly university.
As part of university health management, RWTH offers a variety of health, advice and prevention services (e.g. university sports). There is an extensive range of further training courses and the opportunity to purchase a job ticket for collective bargaining employees and civil servants.
The job advertisement is aimed at all genders.
We particularly want to promote the careers of women at RWTH Aachen University and therefore welcome female applicants.
Women will be given preferential consideration if they have the same suitability, skills and professional performance, provided they are underrepresented in the organizational unit and provided that reasons relating to a competitor do not outweigh them.
Applications from suitable severely disabled people are expressly welcome.
In the interests of equal treatment, we ask you not to take an application photo.
Information on the collection of personal data in accordance with Articles 13 and 14 of the General Data Protection Regulation (GDPR) can be found at https://www.rwth-aachen.de/dsgvo-information-bewerbung.

Salary/remuneration

TV-L EG 13

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