I am a Master's student in Theoretical and Computational Physics at EPFL (Switzerland). My research interests lie at the intersection of statistical physics, machine learning dynamics, and quantum computing. I combine a rigorous theoretical background with strong software engineering practices to build scalable computational tools for scientific research.

• Investigating the learning dynamics and structural priors of Transformer attention mechanisms (SPOC Laboratory, EPFL)
• Simulating quantum algorithms and evaluating hardware noise limits on real IBM Quantum devices
• Developing robust backend software and APIs for spacecraft operations (EPFL Spacecraft Team)

• Advanced topics in Statistical Physics, Reinforcement Learning, and Computational Quantum Physics
• The mathematical foundations of deep learning (spectral signatures, eigenvalue distributions, etc.)
• High-performance scientific computing and systems programming (Rust, C/C++, Python)

• Open-source scientific computing, quantum information, or theoretical ML projects
• Implementations of novel neural network architectures or physical simulations
• Projects bridging the gap between theoretical physics and applied data science

• Theoretical physics, quantum mechanics, and statistical modeling
• Machine learning theory (e.g., PSD geometry, semantic similarity in attention mechanisms)
• Building robust computational pipelines and backend architectures

• Email: victor.peucelle@epfl.ch
• LinkedIn: linkedin.com/in/victor-peucelle

• In my spare time, I run Studio Bernoulli—a personal coding sandbox where I experiment with bridging abstract physics concepts, rigorous mathematical modeling, and creative software development!