M2 - Computational Physics (PhysNum)

Mathematics for Computational Physics. Introduction to tools for the study of partial differential equations (distributions, variational formulation, Sobolev spaces).

Introduction to integral methods and their numerical implementation. Applications to diffraction problems in the harmonic regime.

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This course is designed to provide students with skills in the numerical solution of the Schrödinger equation in order to simulate complex quantum well structures. The course begins with a study of cases where the solution is analytical, followed by cases where the solution is semi-analytical, before moving on to the finite difference (FD) method. Various FD schemes are presented, each accompanied by an evaluation of convergence as a function of key parameters (domain truncation, number of samples, etc.). Finally, examples of concrete physical applications are examined.    

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This course lays the groundwork for using “atomistic” simulation tools—that is, those based on microscopic interactions between constituents. Primarily, it lays the foundation for simulations known as “Molecular Dynamics” and “Monte Carlo.”

It covers the underlying theoretical concepts to build a solid understanding of the methods, as well as the practical implementation of the corresponding code.

The critical and reasoned analysis of data is also discussed.

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This course is an introductory course with no prerequisites on scientific image processing, in the context of both physics and the medical sciences.

Starting with the fundamentals of digital image coding, we will introduce the main techniques aimed first at improving image quality and then at extracting quantitative data from images. The course will cover deconvolution, noise reduction, thresholding, segmentation, Fourier transforms, and wavelets.

We will conclude by discussing the specific challenges posed by image sequences (films) or 3D images, such as MRI data in a medical context.

The tool used will be the MATLAB/Octave programming environment.

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This course is an introduction to artificial intelligence for physicists. It aims to explore applications of deep learning using the TensorFlow and Keras libraries. It includes a presentation of examples of how deep learning is used in physics.

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English tutorial course for students in the Master 2 Physics program who are preparing for a career in an English-speaking professional environment in a contemporary context.

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This module gives students the opportunity to learn about the unique aspects of the professional world and to prepare to enter it under the best possible conditions, particularly through sharing experiences with professionals in the field. Students practice successfully completing the job application process in a systematic manner by thoroughly analyzing job postings, crafting targeted resumes and cover letters, and preparing for job interviews (through role-playing and simulations).

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This course unit covers the computer-based solution of electromagnetism problems. Using Maxwell’s equations, it demonstrates how to simulate the behavior of electromagnetic waves in various media. In particular, it includes a detailed implementation of simulations based on the finite difference time domain (FDTD) method.

An introduction to diffraction problems in the harmonic regime caused by a bounded obstacle will be provided for the case of scalar waves in 2D and 3D.

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This module provides an introduction to advanced applications of atomistic simulation methods, particularly molecular dynamics.

It thus involves expanding on the methods already learned, both in the field of physics (ab initio simulations, density functional theory) and in terms of implementation (optimization, parallelization) and practical application (an introduction to conducting simulations in a high-performance computing environment).

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A 5-ECTS supervised project during which students work individually to develop software for research and development and/or teaching.

This project builds on the experience gained during the supervised project completed in the first year of the master’s program. This time, students work individually, which offers a different experience from the group-based project in the first year of the master’s program. Students are assigned the task of developing software that meets specific requirements and must deliver functional code.   

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A six-month M2 internship (25 ECTS) completed at a company or public organization (research laboratory, national agency, etc.).

The internship must focus on a physics problem that involves a numerical computation component.  

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