Postdoctoral Researcher: How do physical learning systems learn?

Work Activities
We are seeking an excellent and motivated postdoctoral researcher to join our team at AMOLF, working on fundamental questions on physical self-learning systems as part of the NWO ENW‑M1 project “How do physical learning systems learn?”. The research position is intended to start in September 2026.

Physical learning is an emerging paradigm in which materials adapt their behavior through local physical rules, without digital computation. Despite rapid experimental progress, it remains poorly understood how such systems learn and what signatures learning leaves in their physical structure and energy landscape. This project aims to build the theoretical foundations of physical learning, uncovering the modes of learning available to linear and nonlinear systems, their expressiveness and capacity, and the physical imprints of learned tasks.

The postdoctoral researcher will contribute to developing this theoretical framework, with a strong focus on analytical modeling, computational methods, and the interpretation of learning signals embedded in physical structures. Recent advances in our group, including new methods for detecting learning signals in linear networks that reveal aspects of the tasks they have learned, provide a powerful conceptual starting point.

The scope of possible topics includes:

• Developing theoretical tools to characterize learning modes in linear and nonlinear physical networks.
• Understanding how learning reshapes physical energy landscapes.
• Identifying physical signatures of learned tasks.
• Exploring expressiveness, capacity, and continual learning in physical systems.

This position is theoretical and computational in nature, with opportunities for collaboration with experimental groups working on physical learning in electronics, mechanics, and living flow networks (Physarum Polycephalum).

For more information about our work, see:

[1] Stern, Hexner, Rocks and Liu, Supervised learning in physical networks: From machine learning to learning machines, PRX 11, 021045 (2021)

[2] Stern and Murugan, Learning without neurons in physical systems, Ann Rev Cond Matt Phys 14, 417 (2023)

[3] Stern, Liu and Balasubramanian, Physical effects of learning, PRE 109, 024311 (2024).

[4] Stern, Guzman, Martins, Liu and Balasubramanian, Physical networks become what they learn, PRL 134, 147402 (2025).

Qualifications
We seek candidates with:

• A PhD in physics, applied mathematics, materials science, mechanical engineering, computer science, or a related field.
• Strong interest in learning, adaptation, and dynamical systems in physical contexts
• Experience with analytical and\or computational modeling.
• Proficiency in numerical methods and coding (Python, JAX, MATLAB, or related tools).
• Good communication skills in English.
• Experience with complex systems, energy landscapes, physical memory, machine learning, or soft/active matter is advantageous but not required.
• We welcome applicants from diverse backgrounds and strongly encourage curiosity-driven thinkers.

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 Learning Machines group at AMOLF, led by Menachem (Nachi) Stern, 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 AMOLF resources in all aspects of design, realization, and interpretation of computational models of physical learning systems. We have a strong focus on stimulating development of personnel 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 working atmosphere at the institute 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
• Salary is in scale 10 (CAO-OI) which starts at 4.552 Euro’s gross per month, and a range of employment benefits.
• AMOLF assists any new foreign Postdoc with housing and visa applications and compensates their transport costs and furnishing expenses.

More information?
For further information about the position, please contact

Dr. Menachem Stern
E-mail: stern@amolf.nl

Application
You can respond to this vacancy online via the button below.

Online screening may be part of the selection.

Diversity code
AMOLF is highly committed to an inclusive and diverse work environment: we want to develop talent and creativity by bringing together people from different backgrounds and cultures. We recruit and select on the basis of competencies and talents. We strongly encourage anyone with the right qualifications to apply for the vacancy, regardless of age, gender, origin, sexual orientation or physical ability.

AMOLF has won the NNV Diversity Award 2022, which is awarded every two years by the Netherlands Physical Society for demonstrating the most successful implementation of equality, diversity and inclusion (EDI).

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