PhD Fellow in Physical Learning & Dynamic Systems

AMOLF; Netherlands; On-site; EUR 32,000 - 42,000; Full time

Benefits offered by this job

Housing assistance

Visa support

Transport and furnishing cost coverage

Job summary

AMOLF, located at Amsterdam Science Park, invites applications for a PhD student in the Learning Machines group to study learning in dynamical physical systems within an ERC Starting Grant project. The work blends theory and large-scale simulations to uncover how local interactions govern learning in matter far from equilibrium.

Ideal candidates have a physics or related background, interest in learning theory, and strong programming and numerical skills.

Qualifications

• PhD candidates must meet the MSc degree requirement.
• Strong background in physics and learning theory is desirable.
• Good verbal and written English communication skills.

Responsibilities

• Develop local learning rules for dynamical physical systems.
• Compare physical learning approaches to gradient-based methods.
• Investigate when physical learning succeeds or fails.
• Explore the role of feedback and non-equilibrium dynamics in learning.
• Develop efficient simulation tools for adaptive networks.
• Identify scaling laws and universal features of learning.

Skills

Physics background

Learning theory

English communication

Education

MSc degree

Tools

Python

MATLAB

Job description

AMOLF, located at Amsterdam Science Park, invites applications for a PhD student in the Learning Machines group to study learning in dynamical physical systems within an ERC Starting Grant project. The work blends theory and large-scale simulations to uncover how local interactions govern learning in matter far from equilibrium.

Ideal candidates have a physics or related background, interest in learning theory, and strong programming and numerical skills.