Program Overview

Program Overview

The M2 Computational Neurosciences and Neuroengineering program is a full-time academic program that aims to train students to face problems raised by brain perception, processing, and transmission of information. The program combines experimental, computational, and theoretical approaches, drawing from neurosciences, physics, applied mathematics, and computer sciences at different scales and organizational levels.

Objectives

The primary objective of the program is to present concepts, technological achievements, methodological approaches, and research challenges in computational neurosciences and neuroengineering. It also aims to raise students' awareness of theoretical, experimental, applicative, entrepreneurial, and ethical themes in neurosciences using concepts from physics and engineering sciences.

Skills

To use adequate models, methods, experiments, and technological tools.

Career Opportunities

Career prospects include:

• Engineer (research and development, control, production) in health, pharmacy, agro-food, biotechnology, instruments, and reactants
• Researcher or lecturer in fundamental or applied research in biology, health, or ecology
• Data scientist, data analyst, or engineer in machine learning in innovative sectors (tech, finance, health, energy)
• PhD opportunities in statistical learning, artificial intelligence, or advanced data analysis

Admission Route

The program is designed for students from diverse academic backgrounds, including life sciences, computer science, mathematics, physics, and engineering. Admission requirements include:

• Level equivalent to Master 1 or Master 2
• Level equivalent to an engineer degree
• English level equivalent to B2 certification

Application Period

The application period is from February 15, 2026, to June 30, 2026.

Supporting Documents

Compulsory supporting documents include:

• Copy of diplomas
• Motivation letter
• Letter of recommendation or internship evaluation
• List of other masters requested (excluding Saclay)
• All transcripts of the years/semesters validated since the high school diploma
• Curriculum Vitae
• Fiche de renseignements

Additional supporting documents may be required.

Fees and Scholarships

The amounts may vary depending on the program and personal circumstances. More information on tuition fees and scholarships is available.

Location

The program is located in SACLAY.

Program Details

The Master's program is divided into two semesters, the first of which consists of six teaching units and a supervised project. The second semester is devoted to a 5-6 month internship. The program targets students with a range of backgrounds and aims to provide a high-level course program with international visibility. The training program is based on experimental, computational, and theoretical approaches, combining neurosciences, physics, applied mathematics, and computer sciences at different scales and organizational levels. The program also includes a research internship, which gives students real research experiences in computational neurosciences and neuroengineering. They will have the opportunity to work closely with a leading research team in academic laboratories and opportunities will be created to work on industry-led projects. They will benefit from the supervision of experienced researchers. The project can be carried out with a research group at University Paris-Saclay, with an industrial partner, or with a research institute in France or worldwide.

Capacity

There are 16 available places in the program.

Public and Prerequisites

The program is designed for students from diverse academic backgrounds, including life sciences, computer science, mathematics, physics, and engineering. The prerequisites include:

• Level equivalent to Master 1 or Master 2
• Level equivalent to an engineer degree
• English level equivalent to B2 certification

Research Areas

The program covers various research areas, including:

• Physiological bases of neurosciences
• Neural bases of perception
• Techniques for measuring and stimulating neural activity
• Processing and analysis of neural signals
• Dynamic systems in neuroscience

Further Study Opportunities

Further study opportunities include:

• PhD in statistical learning, artificial intelligence, or advanced data analysis
• Researcher or lecturer in fundamental or applied research in biology, health, or ecology
• Data scientist, data analyst, or engineer in machine learning in innovative sectors (tech, finance, health, energy)