The Institute of Structures and Design works on the development and optimization of materials and their processing and joining technologies, as well as new design approaches and the construction of full-scale demonstrators. Naturally, testing and validation in specialized test facilities and flight trials are also part of the daily work. The focus is on fiber-ceramic, polymer, and hybrid composite materials. The research of new multidisciplinary design tools and digital models forms the basis for hardware development.
What to expect
In the DLR Project “Agile hochratenfertigung und optimierte inspektion für Kleinflugzeuge (AUDITOR)”, we aim to reduce the number of required material testing through a data-driven approach. Incorporating machine learning (ML) models could allow us to accelerate composite structure development while reducing associated costs and testing efforts.
Machine learning has shown promise in accurately predicting complex material properties from data. However, in safety-sensitive domains such as healthcare and the aerospace industry, the reliability of these models is crucial for decision-making process. These industries require trustworthy ML predictions and high confidence.
Neural networks (NNs) naturally carry epistemic (or model) uncertainty arising from limited data and the chosen model architecture. Unlike classical NNs, Bayesian neural networks (BNNs) treat their weights as random variables and learn a posterior distribution over these weights from the training data. Then, the output of BNNs is a distribution over predictions, reflecting uncertainty in the model.
This project aims to ensure the reliability of ML-based predictions regarding material properties by implementing deep learning methods. To achieve this, you will implement BNNs and state-of-the-art inference methods. Additionally, you will leverage domain knowledge to generate physically consistent predictions and reduce spurious uncertainty. Finally, we will establish evaluation protocols for measuring uncertainty.
Your profile
- Sytematic literature review on Bayesian statistics for reliability of NNs.
- Implement Bayesian neural networks (BNNs) for material property prediction and compare inference techniques to approximate the posterior over model weights.
- Embed domain knowledge into the Bayesian framework to assess whether physics-informed priors improve calibration and reduce epistemic uncertainty.
- Establish evaluation protocols for model reliability by computing statistical metrics to quantify model uncertainty.
Your profile
- Ongoing Master's degree in the field of aerospace engineering, mechanical engineering, computer science or comparable degree programmes.
- Very good programming skills in the Python.
- Experience in the field of machine learning and data analysis.
- Experience with collaborative software development in Gitlab is desired.
- Personal responsibility, structured way of working and commitment.
- Proficient in English (written and oral).
- This work can optionally extend to mater’s thesis.
We look forward to getting to know you!
If you have any questions about this position (Vacancy-ID 3135) please contact:
Nathalie Toso / Sanghyun Yoo
Tel.: +49 711 6862 564 / +49 711 6862-573
