WORK ACTIVITIES

We are seeking a motivated PhD student to join our team working on realizing learning in novel physical materials, as part of a joint theoretical/experimental research project between AMOLF and the University of Amsterdam (UvA).
Living systems capture our imagination in their incredible resilience and ability to adapt and prosper in the face of change in their environments. In comparison, human-made materials work reliably until an external change or internal aging cause them to fail once and for all. In this project, we will utilize a physical learning approach to imbue metamaterials and robots with intrinsic adaptation and learning from their experiences.
Using a combination of theory, numerical experiments and precision desktop experiments, we will create 3D materials with self-adapting elastic elements that counteract changes in the environment and the aging of their own parts. We will study how to make these materials learn continually by adapting functions over their lifetime without forgetting old lessons. Thereby, we will bring synthetic materials a large step closer to their living counterparts. With this project, we aim to redefine the way we engineer materials with direct ramifications in adaptive materials and robotics.
We offer a PhD position that combines theoretical exploration and experimental realization of a new class of robotic learning metamaterial, based on active and non-reciprocal elastic elements with controllable stiffness. The project will involve analytical and computational modelling, as well as designing and conducting lab experiments. Key questions include: How to create materials that can self-learn bulk visco-elastic properties? How to structure such materials to learn continually and counteract the aging of their own parts? Can we optimize self-learning materials to achieve properties that are hard to combine? With this research, we aim combine materials engineering with evolution and learning theory, blurring the lines between synthetic materials and adapting living systems.
For more information about our work, see:
[1] Jonas Veenstra, Colin Scheibner, Martin Brandenbourger, Jack Binysh, Anton Souslov, Vincenzo Vitelli, and Corentin Coulais. Adaptive locomotion of active solids. Nature, 639(8056):935–941 (2025).
[2] Yao Du, Jonas Veenstra, Ryan van Mastrigt, and Corentin Coulais. Metamaterials that learn to change shape. arXiv:2501.11958 (2025).
[3] Stern and Murugan, Learning without neurons in physical systems, Ann Rev Cond Matt Phys 14, 417 (2023)
[4] Dillavou, Beyer, Stern, Liu, Miskin and Durian, Machine learning without a processor: Emergent learning in a nonlinear analog network, PNAS 121, e2319718121 (2024)

QUALIFICATIONS

We seek candidates with a strong background in physics, mechanical engineering, materials science, or computer science with an interest in complex meta-materials and (physical) learning. Excellent candidates with training in any area of science or engineering will be considered. PhD candidates must meet the requirements for an MSc degree. Good verbal and written communication skills in English are required. Other advantageous qualities include experience with coding (Python\Matlab) and numerical methods, as well as familiarity with concepts in complex systems, physical memories or machine learning. We strongly believe in the benefits of an inclusive and diverse research environment, and welcome applicants with any background.

Please refer the Job description for details