Nils Coutant

Physics student at EPFL.
This website highlights my academic background, experiences, and selected projects in physics, numerical methods, and programming.

Students 4 Students

Future

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  • Volunteered to teach incoming EPFL students the basics of linear algebra and classical mechanics before the start of the semester.
  • Delivered lectures in a lecture theatre.
  • Prepared course material and exercise sheets.
  • Answered students' questions.

Erasmus Student Network

Future

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  • Helped welcome incoming exchange students to EPFL.
  • Organized social events to help international students connect.

Imperial College London

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  • Completed a third-year Bachelor's exchange at Imperial College London.
  • Took advanced courses, including some Master's-level modules.
  • Became more independent by adapting to a new university system and living in an international environment.
  • Met many people from different countries and cultures.

Bachelor Project

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  • Studied Simulation-Based Inference for rare particle decays.
  • Learned the basics of neural networks and Simulation-Based Inference by myself.
  • Built and tested simple models using simulated data.
  • Discovered what research looks like and attended the VERaiPHY online workshop on machine learning methods for physics.
Expected Coverage Test
Expected Coverage Test
Uncertainty vs events
Average inference uncertainty as a function of the number of events per sample
Many posterior predictions
Several posterior predictions. The red lines indicate the true values of \(C_9\).

Teaching Assistant

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  • Answered students' questions during exercise sessions.
  • Graded exams and student projects.
  • Volunteered to correct an exercise in a lecture theatre.
  • Gave students feedback on their mathematical writing while correcting exercises.
  • Became more comfortable explaining technical concepts clearly to students.

Bachelor Project

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  • Studied Simulation-Based Inference for rare particle decays.
  • Learned the basics of neural networks and Simulation-Based Inference by myself.
  • Built and tested simple models using simulated data.
  • Discovered what research looks like and attended the VERaiPHY online workshop on machine learning methods for physics.
Expected Coverage Test
Expected Coverage Test
Uncertainty vs events
Average inference uncertainty as a function of the number of events per sample
Many posterior predictions
Several posterior predictions. The red lines indicate the true values of \(C_9\).

Ideal Gas Simulation

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  • Started this project as a first-year programming assignment at EPFL and later expanded it out of personal interest.
  • Built an ideal gas simulation based on hard-sphere collisions.
  • Used the simulation to explore concepts such as the ideal gas law, Dalton's law, Brownian motion, and thermodynamic cycles.
  • Gained experience in numerical simulation, object-oriented programming, and scientific visualization.
Simulation of an ideal gas (red: helium, blue: neon, green: argon)
Stirling cycle (colors indicate particle energy)
Particle trajectories over time
Linearity of p and T
Carnot Cycle
Stirling cycle

Discord Bot

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  • Developed a Discord bot independently before starting my studies at EPFL, using Kotlin and object-oriented programming.
  • Built a leveling system with experience points and leaderboards to encourage activity in Discord communities.
  • Had the bot officially verified by Discord after a review process.
  • Reached over 2,000 servers before shutting the project down at the start of my university studies.
  • Learned about branding, data handling, user privacy, and maintaining a project used by real users.
Discord bot screenshot 1 Discord bot screenshot 2 Discord bot screenshot 3 Discord bot screenshot 4 Discord bot screenshot 5

Determinant Visualization

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  • Created a short project using Manim, a Python library for mathematical animations.
  • Used animation to show the geometric meaning of the determinant of a linear transformation.
  • Gained experience with mathematical visualization and Python-based animation tools.

Labs and Computational Physics

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  • Simulated physical systems such as quantum particles, wave propagation, orbital motion, and chaotic dynamics.
  • Used numerical methods including finite differences, Runge–Kutta integration, Verlet schemes, and Crank–Nicolson.
  • Conducted 12 physics lab experiments, with data analysis, uncertainty estimation, and scientific report writing.
  • Gained experience with Python, Matplotlib, and LaTeX.
Gaussian wave packet crossing a potential barrier
Poincaré section showing phase-space structure
Trajectories of the asteroid
Asteroid trajectories around the Sun
Asteroid in L4
Asteroid trajectory near the L4 Lagrange point
Photovoltaique
Photovoltaic cell power vs voltage
Rendement moteurs
DC motor efficiency vs frequency
Spectre rayons X
X-ray diffraction pattern of NaCl
Spectre hydrogene
Emission spectrum of hydrogen

Quantum Field Theory

Imperial College London — 7.5 ECTS

General Relativity

Imperial College London — 7.5 ECTS

Advanced Particle Physics
Nuclear and Particle Physics

Imperial College London — 7.5 + 7.5 ECTS

Quantum Information
Quantum Mechanics

Imperial College London, EPFL — 7.5 + 5 ECTS

Computational Physics
Data Science for Physicists

EPFL — 6 + 4 ECTS

Solid State Physics

Imperial College London — 7.5 ECTS

Analytical Mechanics

EPFL — 5 ECTS

Statistical Mechanics
Probability and Statistics

Imperial College London, EPFL — 7.5 + 3 ECTS

Labs II
Metrology

EPFL — 10 + 4 ECTS

Physics IV III II I

EPFL — 5 + 6 + 7 + 8 ECTS

Analysis IV III II I
Mathematical Methods for Physicists

EPFL — 4 + 5 + 8 + 8 + 3 ECTS

Object-Oriented Programming
Information, Computation and Communication

Self-taught, EPFL — 4 + 6 ECTS

Skill: Simulation-Based Inference and Neural Networks

Self-taught

Skill: LaTeX, Scientific Writing

EPFL

Skill: Python, Kotlin, C++, PyTorch, NumPy, Matplotlib

Self-taught, EPFL

Skill: Teaching

EPFL