• 13 results
Algorithms and computation

Computational physics plays a central role in all fields of physics, from classical statistical physics, soft matter problems, and hard-condensed matter. Our goal is to cover the very basic concepts underlying computer simulations in classical and quantum problems, and connect these ideas to relevant contemporary research problems in various fields of physics. In the TD’s you will also learn how to set, perform and analyse simple computer simulations by yourself. We will use Python, but no previous knowledge of this programming language is needed.

Quantum Computing

The goal of this course is to introduce the main concepts and challenges of quantum computing, a new set of technologies and techniques that promise to solve hard computational problems.

 

a quantum circuit
Numerical methods for fluid dynamics

Numerical simulation is playing an expanding role in the study of fluid dynamics and often complements experiments and theory. In this course, we will introduce and analyse the various methods available to solve the partial differential equations relevant to fluid dynamics. We will study their application to a wide variety of problems and highlight the effects of discretisation strategies. The objective of the course is to gain a practical knowledge, but also a general view of the existing methods and the ability to decide on the best suited choice for a given problem.

 

Fluid Flow
Physics of multicellular systems

The development of animals, starting from a single cell to produce a fully formed organism, is a fascinating process. Its study is currently advancing at a rapid pace thanks to combined experimental and theoretical progress, with yet many fundamental questions remaining to be understood. 

This course will address the fundamental theoretical concepts underlying the self-organization of multicellular systems, from genetic regulation to the mechanics of active biological materials. The course will be based on various concepts of theoretical physics: dynamical systems, soft an active matter, mechanics of continuous media, numerical modeling, etc.

Python pour scientifiques I : traitement des données et interfaçage d’expériences

Que ce soit pour la modélisation, l’acquisition ou l’analyse de donnée, l’informatique est devenu un outil indispensable pour tout scientifique. L’objectif principal de ce cours est d’apprendre à utiliser les techniques permettant de manipuler les données. 

Machine Learning

Statistical machine learning is a growing discipline at the intersection of computer science and applied mathematics (probability / statistics, optimization, etc.) and which increasingly plays an important role in many other scientific disciplines.