Are you interested in researching at the intersection of condensed matter physics, machine learning, and non-equilibrium statistical mechanics? AMOLF is currently seeking a motivated and talented PhD student to join the newly established Learning Machines group in Amsterdam, Netherlands. This doctoral position is part of an ERC Starting Grant project on Physical Learning in Dynamical Systems (PhyLDS).
The successful candidate will work under the supervision of Dr. Menachem (Nachi) Stern to develop a new theoretical framework for how learning emerges in dynamical physical systems operating far from equilibrium. If you have a background in physics, engineering, or computer science and a passion for complex dynamical systems, this is a unique opportunity to help shape a new physics of adaptive matter.
Overview of the PhD Position
Below is a summary of the key details regarding this fully funded PhD scholarship:
| Detail | Information |
|---|---|
| Designation | PhD Student / Doctoral Researcher |
| Host Institution | AMOLF (part of NWO-I) |
| Research Group | Learning Machines Group (led by Dr. Menachem Stern) |
| Research Area | Physical Learning, Complex Systems, Condensed Matter Physics, Machine Learning |
| Location | Amsterdam Science Park, Amsterdam, Netherlands |
| Employment Type | Full-time (40 hours/week) for 4 years |
| Monthly Salary | Starting at €3,115 gross per month (with progressive scales) |
| Application Deadline | Open until filled (Apply as soon as possible) |
Research Area & Project Description
Learning is traditionally considered a computational process restricted to brains or silicon chips. However, adaptive materials and biological networks continuously modify their behavior based on historical experience. The PhyLDS project aims to build a comprehensive theoretical understanding of how learning emerges in dynamical physical systems operating far from equilibrium.
Unlike traditional artificial intelligence that relies on global backpropagation algorithms, physical systems learn via local interactions and physical feedback. This project combines analytical theory and large-scale numerical simulations to study adaptive dynamical networks (such as mechanical networks, flow networks, and neuronal systems). As a PhD student, you will contribute to:
- Developing local learning rules tailored for physical dynamical systems.
- Comparing physical learning frameworks with idealized gradient-based machine learning methods.
- Investigating the thresholds where physical learning succeeds or fails.
- Exploring the impact of feedback, non-equilibrium dynamics, and task complexity.
- Coding and deploying efficient simulation tools for adaptive dynamical networks.
- Identifying universal features, scaling laws, and phase boundaries of learning in matter.
Eligibility & Qualifications
Candidates from all over the world are welcome to apply. To be considered, you must meet the following requirements:
- Academic Qualification: Must hold (or be close to completing) an MSc degree in Physics, Mechanical Engineering, Materials Science, Computer Science, or a closely related science/engineering discipline.
- Language Skills: Excellent written and verbal communication skills in English are mandatory.
- Core Interests: A strong interest in learning theory, condensed matter physics, complex networks, or physical memories.
- Preferred Technical Skills: Prior experience with programming (e.g., Python, MATLAB) and numerical methods is highly advantageous.
Scholarship Benefits & Working Conditions
AMOLF offers a highly collaborative and supportive international environment with competitive employment terms under the Collective Labour Agreement (CAO-OI):
- Full-time Contract: A 4-year contract (40 hours per week) supervised jointly by AMOLF and a Dutch University where you will eventually defend your PhD thesis.
- Competitive Salary: Starts at €3,115 gross per month in the first year, alongside attractive year-end and holiday bonuses.
- Relocation Assistance: For international candidates, AMOLF provides extensive support with visa applications, finding housing, and reimbursing travel/furnishing expenses.
- Professional Training: Access to specialized courses designed to help PhD students build valuable academic and transferable skills.
- Inclusive Environment: Working in a diverse, collaborative environment that values equality, diversity, and inclusion (EDI)—AMOLF is a recipient of the prestigious NNV Diversity Award.
How to Apply
Interested candidates can apply directly through the online vacancy portal. You will be required to fill out a brief personal information form and upload your application documents (including your CV and a motivation letter).
For specific inquiries regarding the research project, please contact:
Dr. Menachem Stern
Email: stern@amolf.nl
To submit your application, please click on the official link below:
Apply Online for the PhD Position at AMOLF
Last Date to Apply
There is no strict closing date mentioned; however, applications are evaluated on a rolling basis. Interested candidates are strongly encouraged to submit their applications as soon as possible.
