Internship

We are now accepting applications for our internship program. A strong background in mathematics, physics, and machine learning is required. The program is structured in three phases:

Phase I: Completion of an online course covering advanced topics in machine learning, such as statistical modeling, thermodynamic formulations, non-equilibrium dynamics, and learning algorithms.

Phase II: Participation in a research project focused on modeling learning systems—biological, physical, or artificial—using numerical methods. This may include developing simulations or performing statistical analyses.

Phase III: Engagement in a theoretical research project involving the analytical modeling of learning systems. This may involve formulating new theories or applying existing frameworks to biological or physical systems.

We especially encourage applicants who possess deep theoretical knowledge, even if they lack hands-on experience.

Lectures

Lecture #1: Neural septuple and unified framework

Lecture #2: Learning, activation and boundary dynamics

At the Intersection of Sciences

Internship

To apply please fill out this form and attach your resume xor curriculum vitae:

Lectures

Lecture #1: Neural septuple and unified framework

Lecture #2: Learning, activation and boundary dynamics

Lecture #3: Statistical mechanics of learning

Lecture #4: Thermodynamics of learning

Lecture #5: Non-equilibrium learning dynamics

Lecture #6: Physics and Bio-inspired algorithms

Lecture #7: Efficiency of learning and entropy production

Lecture #8: Self-organized criticality and scale invariance

Lecture #9: Autonomous particles and emergent fields

Lecture #10: Multilevel learning and phase transitions