The Laboratory for Product Development and Lightweight Design (LPL) focuses on the design and optimization of complex technical systems. We develop methods, tools, and specific solutions to technical problems with optimal functionality, weight, and cost. Currently, we are looking for a research assistant (m/f/d) to develop computational design methods for smart CFRP structures

Topic
CFRP composites enable high mechanical performance, e.g., related to strength and stiffness, while maintaining little weight. High lightweight performance is to be further improved by sensor measurement data combined with machine learning. Computational design tools based on Finite Element analysis, numerical optimization and machine learning methods are to be developed and applied to several projects with industry partners.
In a first project (others to follow), smart CFRP rims for racing bikes and high-performance vehicles are to be designed, manufactured and tested. The mass and stiffness of vehicle rims significantly influence important driving dynamic properties and fuel consumption. As rims are also particularly relevant to road safety, the lightweight design potential usually is not fully exploited. Thus, high safety factors against failure increase weight and manufacturing costs. To avoid this, an integrated sensor system should predict and detect damages. As part of a research project, a structural health monitored CFRP lightweight rim is to be designed and optimized. The shape and layer structure design must consider the constraints imposed by manufacturing and integrating a sensor system. The present sensor technology – based on deformation measurement – is to be integrated to detect any critical damage. Relevant training data are to be gathered and evaluated based on suitable coupon and component tests. Appropriate machine learning algorithms are to be adapted for reliable damage prediction and detection. These results may eventually be used to define a predictive maintenance procedure applicable to other CFRP applications.

Tasks
• Consolidation and application of lightweight design methods based on Finite Elements and numerical optimization
• Design of sample structures
• Possibly material and component tests
• Adaptation of machine learning algorithms for detecting damage based on measured data.
• Support lectures and lab exercises; supervise students.

Required profile (please explain in the cover letter)
• Master\'s degree in mechanical engineering, automotive engineering, aerospace engineering, or similar.
• Solid knowledge of fiber composites (CFRP), numerical optimization, and machine learning.
• Distinctive getting-things-done work attitude.
• Fluent in German or strong commitment to learning German.

We offer
• Innovative topic with industry proximity, supportive and interdisciplinary team culture, the opportunity to graduate with a Ph.D., and possible research stays abroad.
• Full position as research/teaching assistant (m/f/d) with salary according to TV-L.

Application
All applications received by 05.11.2023 will be considered.
Please send your application in German or English (one pdf file, reference code PROVES) by email to:
applications.lpl@ed.tum.de. Please visit our website to check for extensions of the deadline.
Severely disabled persons are given preference in the event of equal suitability and qualifications. TUM promotes equality between women and men.
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