PhD Position Iterative Hybrid Optimization Algorithm

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: Iterative hybrid optimization algorithm

The optimization of aerospace structures often requires hybrid and iterative approaches to function efficiently across different domains (e.g., fluid and structure domains) of the overall problem. This research topic focuses on the automation and adaptivity of the modelling and optimization process using latest ML and QC technologies.