PhD Position Optimization of Composite Laminates

There are many challenges in aeronautical and space structures and materials nowadays. The urgency to reduce greenhouse gas emission in aviation calls for the lightest structures possible without compromising safety. In space, radiation damage to materials poses a serious threat to the safety, durability and reusability of spacecrafts. The designs of future aerospace materials and structures will require high-fidelity computational models to predict their performance under different loading and radiation conditions and powerful optimization algorithms to find the best possible compositions and configurations to minimize their environmental impact. Addressing the bottleneck issues in these problems will require ground-breaking technological and computational solutions.

4 PhD positions are available under the QAIMS (Quantum-enhanced Artificial Intelligence for sustainable Materials and Structural design in aerospace) lab of the Faculty of Aerospace Engineering of TU Delft. They fall in the exciting interdisciplinary area of Machine Learning (ML), Quantum Computing (QC), composites optimization and materials modelling. Through these projects, we aim to establish novel and sustainable processes and solutions, powered by ML and QC, for the design of materials and structures in aerospace.

The supervision will be jointly carried out by a team of academics from the Faculty of Aerospace Engineering (Dr. Boyang Chen, Dr. Yinglu Tang and Dr. Roeland De Breuker) and the Faculty of Electrical Engineering, Mathematics and Computer Science (Dr. Sebastian Feld, Quantum & Computer Engineering and Dr. Matthias Möller, Applied Mathematics) of TU Delft.

PhD Position: Optimization of composite laminates

The latest composite manufacturing technologies such as automatic fibre/tape placement have enlarged the design space of composite structures tremendously. However, this also makes the task of finding the optimal design very challenging with classical means, certainly when taking structural stability, failure and manufacturing constraints into account. In this project, we aim to explore latest ML and QC technologies to set up new optimization algorithms for composites design.