M2 Computational Neurosciences and Neuroengineering

Candidater à la formation
  • Capacité d'accueil
    16
  • Langue(s) d'enseignement
    Anglais
  • Régime(s) d'inscription
    Formation initiale
Présentation
Objectifs pédagogiques de la formation

Presentation
One of the greatest challenges of modern science is to understand how the brain processes information in order to imitate its computing and learning capabilities (Artificial Intelligence, neuromorphic circuits, machine learning,...) and to compensate the brain failures with computational and technological tools (Closed-Loop Neurosciences, Brain Computer Interface,...).
Thus, the Computational Neuroscience and Neuro-engineering Master aims to train students to face problems raised by brain perception, processing and transmission of information. The training program is based on experimental, computational and theoretical approaches, combining neurosciences, physics, applied mathematics and computer sciences at different scales (cell, network, behaviour) and different organizational levels (micro, meso- and macroscopic scales).
Thanks to the reputation of the laboratories and research teams involved, the Master degree offers a very high level courses programme with a high international visibility.

Goals
The target of this Master program is to present the concepts, technological achievements, methodological approaches and research challenges in computational neurosciences and neuroengineering. It also aims to raise students' awareness of the theoretical, experimental, applicative, entrepreneurial and ethical themes of Neurosciences using the concepts of Physics and Engineering Sciences. Students have access to an unique interdisciplinary training, enhancing their skills in research, analysis and scientific presentation and developing their ability to work as part of a multidisciplinary team.

Job opportunities
The CNN Master's degree trains future engineers, researchers and lecturers specialized in Computational Neurosciences and Neuroengineering with an interdisciplinary culture and approaches ranging from theory to experimentation by combining computational methodologies. Engineer jobs and Phd projects in academic, industrial laboratories, integration in R&D departments in France or abroad, represent the main opportunities.

Courses program
The CNN Master courses programme targets students with a range of backgrounds, including Life Sciences, Computing Science, Mathematics, Physics and Engineering. One part of the courses is focused on the theoretical approaches and the remainder is focused on a research project. Student will achieve the CNN Master with his own skills and interests.
During the first semester, lectures such as the physiological bases of neurosciences, the neural bases of perception, the techniques for measuring and stimulating neural activity, the processing and analysis of neural signals, the dynamic systems in neuroscience, will provide the necessary tools to understand the complex phenomena involved in processing and transmitting information in the brain. A supervised scientifc project will complete the students' training during the semester one.
Semester two begins with an research internship of three to six months. This internship gives students real research experiences in computational neurosciences and neuroengineering. They will have the opportunity to work closely with a leading research team in the academic laboratories and opportunities will be created to work on industry lead 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 institut in France or worldwide.

Lieu(x) d'enseignement
ORSAY
Pré-requis, profil d’entrée permettant d'intégrer la formation

The Computational Neurosciences and Neuro-engineering (CNN) Master courses programme targets students with a range of backgrounds, including Life Sciences, Computing Science, Mathematics, Physics and Engineering.

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

  • To solve a problem by employing approximation, simulation and experiments.

  • To develop a wide scientific and technical background in the context of a transdisciplinary approach.

  • To acquire new knowledges and skills in relevant domains (technical, economical or other).

  • To evaluate the relevance of using one method over another based on the scientific issues addressed.

  • To improve skills in writing, analyzing and talking.

Profil de sortie des étudiants ayant suivi la formation

The CNN Master's degree trains future engineers, researchers and lecturers specialized in Computational Neurosciences and Neuroengineering with an interdisciplinary culture and approaches ranging from theory to experimentation by combining computational methodologies.

Débouchés de la formation

Engineer jobs and Phd projects in academic, industrial laboratories, integration in R&D departments in France or abroad, represent the main opportunities in the following fields :
- Neuromorphic comptine tools,
- Neuro-robotics,
- Cognitive and functional stimulation
- Visual, auditory, sensorimotor prostheses
- Technological tools for neurological and neurodegenerative pathologies
- Brain-machine interface
- Nerve signal processing
- Neuro-inspired learning
- Artificial intelligence

Collaboration(s)
Laboratoire(s) partenaire(s) de la formation

Institut des Neurosciences Paris Saclay
Neurospin - DRF/JOLIOT
Laboratoire des Signaux et Systèmes
Inria Saclay-Île-de-France.

Programme

Le premier semestre correspond aux enseignements théoriques et pratiques. Il est constitué de 7 UEs obligatoires.

Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Supervised project 12 50
Physiological bases of neurosciences 3 25 0
Neural bases of perception 3 25
Methods for measuring and stimulating neuronal activity 3 25
Machine learning 3 17 8
Dynamical systems and computational neuroscience 3 17 8
Closed loop neurosciences 3 25

Le second semestre correspond à un stage de recherche à effectuer dans un établissement de recherche académique ou dans le service R&D
d'une entreprise.

Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Internship 30
Modalités de candidatures
Période(s) de candidatures
Du 15/01/2020 au 15/07/2020
Pièces justificatives obligatoires
  • Curriculum Vitae.

  • Lettre de motivation.

  • Tous les relevés de notes des années/semestres validés depuis le BAC à la date de la candidature.

Pièces justificatives complémentaires
  • Dossier VAPP (obligatoire pour toutes les personnes demandant une validation des acquis pour accéder à la formation).

Contact(s)
Responsable(s) de la formation