Abbréviation
M2
M2 Quantum Engineering

The second year is organized as follows:

  • Semester 1 : Lectures and Practicum Courses (30 ECTS).
  • Semester 2 : Internship in France or abroad (30 ECTS).

Note:  The students will have to choose between different optical courses in the first semester

Master Quantum Engineering

PSL’s quantum engineering programme is a 2-year program that provides cutting-edge training. It is spread over two years and 120 ECTS, requesting full-time studies. 

Quantum technologies are a strategic global topic for academia and industry. These disruptive technologies have the potential to revolutionise the design and implementation of information and communication sciences and technologies. France is investing nearly €2 billion in this field over the next four years.

A new interdisciplinary training programme in quantum technologies has been launched by PSL University in September 2022. It results from an unprecedented synergy between some of its founding schools, namely: Ecole normale supérieure, ESPCI Paris, Mines Paris, École nationale supérieure de Chimie de Paris - PSL and the Observatoire de Paris, and it is part of PSL’s graduate programme in physics.


The academic year is divided into two semesters.

  • Semester 1: Courses and Practicums (30 ECTS).
  • Semester 2 : Internship : France or abroad (30 ECTS).
M2 ICFP

The second year is organized into four tracks: Condensed matter physics, Soft matter and biological physics, Quantum physics: From the foundations to quantum technologies, and Theoretical physics.  

Master ICFP

The ICFP  Master is a top-level program designed specifically for the best French and international students.

By choosing the École normale supérieure (ENS), students are assured of benefiting from the prestigious teaching that has made the ENS's reputation since its creation in 1794.

From statistical physics to machine learning & back

In this class, we will explore how the concepts of statistical physics can prove useful to tackle main theoretical questions in machine learning and how the recent progress in generative AI can assist statistical physics computations. We will describe analytical approaches as well as numerical approaches at this intersection. We will present the methods of statistical physics and high-dimensional probability which play a central role in these recent and new research activities, and also make an incursion in some other interdisciplinary applications (biological neural networks and theoretical ecology). 

Some TDs will illustrate the lecture’s topic by analytical computations, others by numerical implementations in python and pytorch.

Wednesday afternoon: Lecture 2pm-3.45pm + TD 4-5:30pm  

Circuits and network dynamics in synthetic biology and neuroscience

This course will explore how living systems make use of circuits and networks to process information from neuroscience to synthetic biology.

Interfaces and morphogenesis

The goal of this course is to better understand how interfaces instabilities can affect morphogenesis. 

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. 

Conformal Field Theory
String Theory

The goal of this course is to introduce string theory. 

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