PhD Candidate: Learning in Adaptive Physical Systems

AMOLF; Amsterdam; Sur place; EUR 32 000 - 38 000; Plein temps

Avantages offerts par ce poste

Housing assistance

Visa support

Transport costs covered

Furnishing expenses

Résumé du poste

AMOLF, located at Amsterdam Science Park, seeks a motivated PhD student to study the theory of learning in dynamical physical systems within the Learning Machines group. The ERC Starting Grant project aims to develop a physics-based framework for learning in adaptive matter under time-dependent dynamics.

You will contribute to developing local learning rules, compare to gradient-based methods, and explore the boundaries of learning in non-equilibrium systems, with access to large-scale

Qualifications

• PhD candidates must meet MSc degree requirements.
• Good verbal and written English communication skills required.
• Experience with coding (Python/Matlab) and numerical methods.

Responsabilités

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

Connaissances

Coding

Numerical methods

English communication

Python

MATLAB

Formation

MSc degree

Outils

Python

MATLAB

Description du poste

AMOLF, located at Amsterdam Science Park, seeks a motivated PhD student to study the theory of learning in dynamical physical systems within the Learning Machines group. The ERC Starting Grant project aims to develop a physics-based framework for learning in adaptive matter under time-dependent dynamics.

You will contribute to developing local learning rules, compare to gradient-based methods, and explore the boundaries of learning in non-equilibrium systems, with access to large-scale