PhD in Physical Learning in Dynamical Systems

Laplace Expert Ltd; Netherlands; Remote; EUR 35,000 - 36,000; Full time

Job summary

AMOLF, Learning Machines group, based in the Netherlands, invites applications for a PhD researcher position focusing on physical learning in dynamical systems. The project explores how local rules enable networks to acquire global behaviors, combining nonlinear physics, adaptive networks, and machine learning under ERC PhyLDS.

Successful candidates will contribute to numerical simulations and theoretical work, with a four-year full-time commitment and a monthly stipend of €3,115, funded through

Qualifications

• Applicants should hold, or be close to completing, an MSc in physics, mechanical engineering, materials science, computer science or a closely related field.
• A strong background in numerical modelling and dynamical systems is expected; experience with complex systems, physical memories or machine learning is advantageous.

Skills

Numerical modelling

Dynamical systems

Complex systems

Machine learning

Education

MSc in physics

MSc in mechanical engineering

MSc in materials science

MSc in computer science

Close to completing MSc

Tools

Python

MATLAB

Numerical methods

Job description

PhD in Physical Learning in Dynamical Systems

AMOLF · Learning Machines group

Netherlands

Apply by 31 Dec 2026

About This Opportunity

AMOLF is recruiting a PhD researcher to study physical learning in dynamical systems within the Learning Machines group. Applications close on 31 December 2026 at 23:59 Europe/Amsterdam.

The project investigates how local physical learning rules can enable dynamical networks to acquire useful global behaviours. The research combines nonlinear and nonequilibrium physics, adaptive networks, numerical simulation and ideas from machine learning within the ERC-funded PhyLDS project.

physical learning dynamical systems adaptive networks nonequilibrium physics machine learning

Level

PhD

Time Commitment

Full-time

Duration

4 years

Reference Code

3558

Field

Physical Learning and Dynamical Systems

Specialization

Physics

Organization Type

Research Institute

Funding & Compensation

Amount EUR 3,115 / Per Month

Funding Type Salary

Funding Status Paid

Period Per Month

Requirements

Education

Applicants should hold, or be close to completing, an MSc in physics, mechanical engineering, materials science, computer science or a closely related field.

Experience

A strong background in numerical modelling and dynamical systems is expected; experience with complex systems, physical memories or machine learning is advantageous.

Technical Skills

• Python
• MATLAB
• Numerical methods
• Dynamical systems
• Complex systems