PhD: Physical Learning for Dynamical Networks

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

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