Teaching
Bachelor 2nd year: Computer lab: Introduction to scientific computing
This course introduces students to essential scientific computing concepts and tools, focusing on programming basics, data analysis, and numerical methods.
Bachelor 3rd year: Problem classes in quantum physics
Students tackle simple problems in quantum mechanics, reinforcing their understanding of fundamental concepts such as wave functions, operators, and quantum states.
Bachelor 3rd year: Simulation of experiments and scientific computing: Numerical integration of PDEs
This course covers numerical methods for solving partial differential equations, with applications in physics.
Master 1: Coordination of the multidisciplinary learning lab (M-DiLL) students projects in physics
I oversee interdisciplinary student projects, guiding teams as they gain a first experience in scientific research to solve real-world problems
Master 1: Numerical experiments: The Monte Carlo and molecular dynamics methods
This course aims to provide students with a thorough understanding of Monte Carlo (MC) and molecular dynamics (MD) methods in computational physics. Students will learn how to implement these techniques, simulate physical systems and analyze the results to extract key properties. Practical exercises will reinforce their skills in using software such as LAMMPS for atomistic simulations.
Master 2: Computer lab: The kinetic Monte Carlo method
This computer lab focuses on the kinetic Monte Carlo method and its applications in studying time-dependent processes in materials science.
Master 2: Nanoobjects: problem classes
Students work through problems related to the physics of nanostructures, exploring their unique properties and potential applications.
Master 2: Applied Machine Learning
This course introduces physics graduate students to fundamental machine learning techniques with wide-ranging applications not only in physics, but also across other scientific disciplines and industry. Students will explore traditional techniques such as linear and logistic regression, decision trees, support vector machines, as well as more modern approaches like neural networks. Through hands-on exercises and real-world examples, participants will learn to extract valuable insights from data by implementing these tools for tasks like regression, classification, dimensionality reduction, and clustering.
Graduate school Orléans numérique (GSON): Introduction to deep learning
This comprehensive course introduces graduate students to the fundamentals and applications of deep learning in scientific research. Over ten sessions, students explore key concepts from basic neural networks to advanced architectures like CNNs and VAEs. The curriculum balances theoretical foundations with hands-on coding exercises using TensorFlow. Topics include backpropagation, autoencoder networks, and real-world applications. Through project work and presentations, students gain practical experience in implementing deep learning solutions, preparing them for cutting-edge research and applications in various scientific domains.
Erasmus Coordinator for Physics Students
As the Erasmus coordinator, I facilitate international exchange opportunities for physics students, providing guidance on academic programs. Don't hesitate to contact me if you wish to participate in an exchange program.