The constraint of embedding ever more complex systems pushes us both to implement advanced algorithms such as those based on AI, making the systems more intelligent, while providing a design effort adapted to the constraints of the application (size, consumption, security, weight, real-time reactivity, computing power, etc.).
It is in this context that the training aims to provide scientific bases in architecture, design methodology, modeling of embedded systems, communications systems, low-consumption systems, reconfigurable systems.
Skills or abilities assessed
- Designing hardware/software architectures, real-time systems
- Modeling architectures for embedded digital and mixed analog/digital systems
- Designing digital and mixed analog/digital systems-on-chips
- To ensure the smooth running of a project
- Write a state of the art
- To conduct scientific experiments and present the results.
Research Project
The research initiation program includes practical work, 150 hours of Research Initiation Project, and a 6-month internship. The practical work involves illustrating or implementing methods presented in the various teaching units. The Research Initiation Project is a synthesis project (including bibliography, theoretical analysis, and practical application) that allows students to delve deeper into one of the Master's program's disciplinary fields and prepare for the research laboratory internship (learning to plan work, write a report, and present research findings).
Internship
The extended internship, lasting 5 to 6 months, constitutes a very important part of the program. It awards 20 ECTS credits out of a total of 60 credits. This internship, approved by one of the program directors, must be carried out on a research topic within a university or industrial laboratory.
Opportunities
Upon completion of the Master's program, graduates will possess all the necessary skills to pursue doctoral studies or careers in industrial R&D within the fields of electronics and embedded systems. The applied nature of the topics covered offers career opportunities in both academia and industry in the following sectors:
- Electronics and embedded systems
- Transportation (Automotive, Aeronautics, Aerospace)
- Telecommunications,
- Robotics,
- Health,
- Security,
- ...
This training course aims to acquire scientific bases in artificial intelligence and robotics, a general culture in cognitive sciences and neurosciences for the “intelligent” and “bio-inspired” processing of information:
- Neural network models
- Optimization algorithms
- New human-machine interface techniques
- Embedded computing and bio-inspired robotic control
Upon completion of the program, students will be able to integrate various machine learning and intelligent information processing technologies (neural networks, AI, real-time decision-making, metaheuristics, pattern recognition). They will have acquired knowledge of the mechanisms of autonomous intelligent systems for prediction, as well as concepts in cognitive science and computational neuroscience.
They will be able to:
- Designing intelligent system architectures
- Developing autonomous learning algorithms
- Design multimodal and advanced Human-Machine Interfaces (HMI) (Tangible Interface, Augmented Reality)
- Process images, index them and use them in systems
- Perform automatic image recognition (pattern, shape, face) and gesture recognition (motion tracking)
- Organize the smooth running of an introductory research project from start to finish, write a state of the art report, implement scientific experiments, present results
Research Initiation Project
The research initiation program includes practical work, 150 hours of Research Initiation Project, and a 6-month internship. The practical work involves illustrating or implementing methods presented in the various teaching units. The Research Initiation Project is a synthesis project (including bibliography, theoretical analysis, and practical application) that allows students to delve deeper into one of the Master's program's disciplinary fields and prepare for the research laboratory internship (learning to plan work, write a report, and present research findings).
Neurocybernetics Team Research Initiation Projects
Internship
The extended internship (lasting 5 to 6 months) is a very important part of the program. It awards 20 ECTS credits out of a total of 60 credits. This internship, approved by one of the program directors, must be carried out on a research topic within a university or industrial laboratory.
Neurocybernetics Team Internships
Partners
Industrial partners: Orange Labs (Issy-les-Moulineaux, Meulan, Lannion), Thales ATM (Bagneux), Thales Communications (Gennevilliers), Thales Services SAS (Osny), Thomson Airsystèmes (Vélizy), Safran (Eragny), Morpho (Osny), EDF (Chatou), EADS (Vernon), Alcatel (Vélizy), Loxane (Cergy), IGN (Saint-Mandé), Gostai (Paris), SNCF (Paris), French Petroleum Institute, ONERA (Arcueil, Palaiseau), DOLABS (Boulogne), METACOM (Magny-Chateaufort), ST Microelectronics (Grenoble), Partnering 3.0 (Cergy), etc.
University laboratories: INRIA (Sophia Antipolis), Arrmines (Paris), ENS (Lyon), Becquerel Hospital Center (Rouen), CEA (Saclay), INSERM (Paris), ENST (Paris), IRISA (Rennes), ...
And abroad: HW Communications Limited (Lancaster, UK), University of Central Lancashire (Preston, UK), Lulea Tekniska Universitet (Lulea, Sweden), Université de Laval (Quebec, Canada), ITT (Illinois Institute of Technology, Chicago, USA), etc.
The DSML program offers a solid foundation in data science, machine learning, statistics and artificial intelligence, training specialists capable of understanding, modeling and effectively exploiting data.
It is based on fundamental courses related to machine learning, data mining, and image processing, supplemented by modules that allow students to deepen their knowledge in areas such as artificial intelligence, databases, optimization, and intelligent system architectures. Students can also take specialized units focusing on deep learning, big data processing, the fairness and transparency of algorithms, or the analysis of complex networks.
These courses enable students to master structured and heterogeneous data, emphasizing techniques for integrating data from diverse sources. They also cover the management of large quantities of images and multimodal data, combining image analysis tools, machine learning techniques, and advanced integration approaches to fully exploit the diversity and complexity of the sources.
The main career opportunities involve research and development in these fields, as well as pursuing a PhD with a view to a career as a teacher-researcher or R&D researcher in a company.
This training course provides the scientific foundations of telecommunications and signal processing applied to vision, imaging, compression, storage and data processing.
The main skills acquired in this course are:
- Analysis and design of communication systems
- Modulation, detection, basics of information theory, error-correcting coding, source compression,
- Resource optimization/allocation
- Protocols for distributed storage systems
- Hardware (embedded) architecture of communication systems
- Signal and data processing
- Advanced filtering methods (wavelets, filter banks, etc.) with applications in compression, imaging, indexing
Students also learn how to manage a research project, that is to say:
- Organize the smooth running of a research initiation project from start to finish,
- Write a state of the art
- Implement scientific experiments
- Present results.
