Postdoctoral Researcher: do physical learning systems

Organisation/Company AMOLF Research Field Physics » Computational physics Physics » Condensed matter properties Researcher Profile Recognised Researcher (R2) Final date to receive applications 2 Sep 2026 - 07:01 (UTC) Country Netherlands Type of Contract Temporary Job Status Not Applicable Hours Per Week 40.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure?

No

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
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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:

• Stern, Hexner, Rocks and Liu
  , Supervised learning in physical networks:
  From machine learning to learning machines,
  PRX 11
  , 021045 (2021)
• Stern and Murugan
  , Learning without neurons in physical systems,
  Ann Rev Cond Matt Phys 14, 417 (2023)
• Stern, Liu and Balasubramanian
  , Physical effects of learning,
  PRE 109, 024311 (2024).
• Stern, Guzman, Martins, Liu and Balasubramanian
  , Physical networks become what they learn,
  PRL 134, 147402 (2025).

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.

AMOLF is a part of NWO-I and initiates 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.(Use the "Apply for this Job" box below)..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,…