Léo Bechet
PhD Researcher · Scientific Computing · Computational Physics
Summary
Passionate about astronomy, optics, and computer science, I have research experience in computational physics, machine learning, and scientific programming. I am currently pursuing a PhD at Uppsala University on energy-efficient algorithmic solutions for data-intensive applications. I enjoy building performant numerical tools and bridging theory with practical engineering.
Education
PhD · Data-Intensive Scientific Computing
Sep 2025 — Ongoing · Uppsala University, Sweden
Energy-efficient algorithmic solutions for data-intensive applications.
Master’s in Physics & Computational Physics (CompuPhys)
Sep 2023 — Jul 2025 · University of Bourgogne Franche-Comté, France
Master 1 completed in 2024, Master 2 completed in 2025.
Bachelor’s in Fundamental Physics
Jul 2021 — Jun 2023 · University of Bourgogne Franche-Comté, France
Research Internships
System-7 Lab, University of Tokyo
Feb 2025 — Jul 2025
Hybrid gradient propagation via DFA-seeding for efficient parallel training of physical neural networks (publication pending).
FEMTO-ST (Time & Frequency Department)
Jun 2024 — Aug 2024
Stepper-motor-based laser sweeping system and PWM control design for cleaning ytterbium deposits in optical setups.
UBFC Computational Physics Projects
2023 — 2024
3D Milky Way extinction mapping with neural networks, potential energy surface modeling in Fortran, and relativistic black-hole simulation/game engine in Python.
Skills
- Programming: Fortran, Python, C, C#, JavaScript, Bash, GLSL/HLSL
- HPC: OpenMP, OpenACC, GPU acceleration, numerical simulation
- ML: TensorFlow, PyTorch, scientific model training workflows
- Tools: Docker, Portainer, LaTeX, Jupyter, Markdown
Languages & Extras
- English (C1+), French (native), Spanish (A2), Japanese (beginner)
- Photography and astrophotography, image processing
- Electronics, embedded systems, optical test-bench experience
- Course-En-Cours 2017 regional winner (team engineering project)
Projects
D2Q9 Lattice-Boltzmann Simulation (WebGL, MRT)
Feb 2026 — Ongoing · Personal project
Implemented a D2Q9 Lattice-Boltzmann fluid simulation with multi-relaxation-time dynamics using WebGL shaders. The solver handles 2D flow around static obstacles and reproduces known physical phenomena.
GPU/CPU Optimized N-Body Simulation in Fortran
Oct 2024 — Dec 2024 · University of Bourgogne Franche-Comté
Built an N-body simulation and optimized it for high performance with OpenMP and OpenACC. Compared implementation speedups and numerical accuracy across CPU/GPU variants.
Stability and Evolution of Conway’s Game of Life
Jan 2024 — Jul 2024 · University of Bourgogne Franche-Comté
Studied cellular automaton stability with hardware-accelerated computation, including filling percentage analysis and Lyapunov exponent exploration under multiple Tychonov distance definitions.
Strange Attractor Visualizer
Mar 2024 — Ongoing · Personal project
Developed a Python/Matplotlib tool for 2D and 3D strange attractor visualization, with a library of well-known systems and support for custom definitions.
Web Implementation of the Marching Cubes Algorithm
Feb 2024 · Personal project
Created a real-time JavaScript 3D demo with dynamic camera controls and optimized chunk management. Implemented Mandelbulb marching cubes with Web Workers for parallelization.
Web-Based 3D Raycast Renderer Development
Feb 2024 · Personal project
Implemented GPU-accelerated ray marching for voxel data in the browser and focused on real-time shader optimization for interactive web graphics.