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Student (f/m/x) - ML-Based Composite Panel Prediction for Wing Preliminary Design
Job Description
Req ID:  4246
Place of work:  Braunschweig
Starting date:  01.05.2026
Career level:  Student employment
Type of employment:  Part time
Duration of contract:  6 months

Remuneration: Remuneration is in accordance with the Collective Agreement for the Public Sector - Federal Government (TVöD-Bund)

Enter the fascinating world of the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e. V.; DLR) and help shape the future through research and innovation! We offer an exciting and inspiring working environment driven by the expertise and curiosity of our 12,000 employees from 100 nations and our unique infrastructure. Together, we develop sustainable technologies and thus contribute to finding solutions to global challenges. Would you like to join us in addressing this major future challenge? Then this is your place!

Would you like to become part of our innovation-driven research team and help shape the future of lightweight system? We develop and test new lightweight construction technologies for resource-saving and climate-friendly structures in the aerospace, transport, energy and security sectors. Our vision is intelligent lightweight system construction for an emission-free tomorrow.

 

What to expect:

Composite structures are key to lightweight aircraft design but require complex optimization to balance stiffness and mass. Conventional analysis of composite panels under varying loads is computationally intensive. By generating a dataset of optimized CLT plates with different dimensions and load cases, a machine learning model can be trained to predict stiffness and mass directly. This enables rapid structural assessments of full wing assemblies without detailed sizing.
The objective of this thesis is to develop and train a machine learning–based surrogate model to predict the stiffness and mass of composite panels as a function of geometry and loading conditions.

You will be responsible for the development of your solution from conception to final implementation. As part of a student job or internship, you will work 10-20 hours per week. In general, it would be desirable to combine this activity with a student research project or thesis.

 

Your tasks:

  • Literature review on composite plate theory and ML applications in structural analysis
  • Parametric variation of plate geometry, loading conditions, stringer geometry and panel optimization
  • Dataset generation and preprocessing
  • Development and training of the ML model
  • Validation against reference data set
  • Application to wing structural weight prediction
  • Documentation and analysis of findings

 

Your profile:

  • Ongoing or completed basic studies in aerospace engineering, computational engineering, mathematics or comparable
  • Basic knowledge in machine learning
  • Basic knowledge of fibre composite structures and of aircraft design
  • Structured way of working
  • Willingness to familiarise yourself quickly and largely independently with a new subject area, motivation and initiative

 

We offer

DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified.

We look forward to getting to know you!

 

If you have any questions about this position (Vacancy-ID 4246) please contact:

 

Dr. David Zerbst
Tel.: 0531 295 1073
Apply now