Webseite Institut für Maschinenelemente und Systementwicklung
The Institute of Machine Elements and System Engineering researches the fundamental structural and tribological behavior of machine elements and represents them in experimentally validated model descriptions. These model descriptions are used to analyse and design the functional, loss and noise behavior of entire technical systems with a focus on drive technology. The developed models are also used to research and develop methods of Model Based Systems Engineering as a central element of future inductive product development processes.
As we navigate the complexities of tribology, the integration of machine learning techniques presents an unprecedented opportunity to reshape our understanding and approach to tribological challenges. Machine learning, with its capacity to analyze vast datasets, identify patterns, and generate predictive models, holds the promise of revolutionizing how we perceive, analyze, and mitigate tribological issues.
The aim of this literature review is to explore and critically analyze the current state of research at the intersection of machine learning and tribology.
Key Areas of Focus:
- Applications of machine learning in predicting friction and wear properties.
- Predictive maintenance and fault diagnosis in tribological systems.
- Solving the physical-chemical tribology equations with machine learning.
- Strong interest in tribology, machine learning.
- Excellent literature review and analytical skills.
- Ability to critically evaluate and synthesize information from diverse sources.
What we offer:
- Contribute to the cutting-edge research at the intersection of these two fields
- Intensive support in a highly motivated and interdisciplinary team
- Gain in-depth knowledge of machine learning applications in tribology.
- Immediate start possible
- Flexible working hours
Auf deine aussagekräftige Bewerbung per E-Mail freut sich:
Ahmed Saleh, M. Sc.
Institut für Maschinenelemente und Systementwicklung
Schinkelstraße 10, 52062 Aachen
Um sich für diesen Job zu bewerben, sende deine Unterlagen per E-Mail an email@example.com