"PhD-student: Self-Learning Metamaterials"

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.

Work environment

AMOLF is a part of NWO-I and initiate and performs leading fundamental research on the physics of complex forms of matter, and to create new functional materials, in partnership with academia and industry. The institute is located at Amsterdam Science Park and currently employs about 140 researchers and 80 support employees. www.amolf.nl. The Machine Materials laboratory at the University of Amsterdam is led by Corentin Coulais, with the goal of developing artificial materials which combine microstructure and out-of-equilibrium processes to interact with their environment in a programmable fashion. The Learning Machines group is a new group at AMOLF, led by Menachem (Nachi) Stern, and focuses on the development of fundamental understanding and theories regarding learning, from a physical perspective, under real world constraints. Our group members work closely together with extensive support from us and AMOLF resources in all aspects of design, realization, and interpretation of computational models of mechanical metamaterials and physical learning systems. We have a strong focus on stimulating development of students in all professional aspects, as well as collaborations with other researchers at our institutes and beyond. Moreover, we work closely together with international groups and companies.

Working conditions

The position is hosted at AMOLF, and the successful candidate will be enrolled for a Ph.D. program at the University of Amsterdam with joint supervision of Dr. Coulais and Dr. Stern. The experimental elements of the work will be carried at the science park campus of the University of Amsterdam. The working atmosphere at AMOLF is largely determined by young, enthusiastic, mostly foreign employees. Communication is informal and runs through short lines of communication. The position is intended as full-time (40 hours / week, 12 months / year) appointment in the service of the Netherlands Foundation of Scientific Research Institutes (NWO-I) for the duration of four years. The starting salary is 2.968 Euro’s gross per month and a range of employment benefits. After successful completion of the PhD research a PhD degree will be granted at the University of Amsterdam. Several courses are offered, specially developed for PhD-students. AMOLF assists any new foreign PhD-student with housing and visa applications and compensates their transport costs and furnishing expenses.