Engineer position in: Machine Learning Meets Reconfigurable Intelligent Surfaces - 11 month contract

Job description

IMT Atlantique

IMT Atlantique, internationally recognized for the quality of its research, is a leading general engineering school under the aegis of the Ministry of Industry and Digital Technology, ranked in the three main international rankings (THE, SHANGHAI, QS).

Located on three campuses, Brest, Nantes and Rennes, IMT Atlantique aims to combine digital technology and energy to transform society and industry through training, research and innovation. It aims to be the leading French higher education and research institution in this field on an international scale. With 290 researchers and permanent lecturers, 1000 publications and 18 M€ of contracts, it supervises 2300 students each year and its training courses are based on cutting-edge research carried out within 6 joint research units: GEPEA, IRISA, LATIM, LABSTICC, LS2N and SUBATECH

The position is open at the campus of Brest within the Mathematical and Electrical Engineering (MEE) Department. MEE department relies on the multidisciplinary expertise of its 37 permanent staff and faculty members. Involved in teaching as well as research, the MEE department brings together specialists in the fields of digital communications, data science and artificial intelligence, as well as in the design of embedded circuits and solutions. The MEE department is part of IMT Atlantique's commitment to constantly adapting teaching and research to the challenges and upheavals affecting society and industry.

In the context of wireless communications evolution towards future 6G networks, the concept of smart radio environments, based on reconfigurable intelligent surfaces (RIS), has been gaining a lot of traction. The idea of being able to change the propagation environment is not only conceptually interesting but also highly beneficial in a variety of scenarios. However, the development of RIS for applications in wireless communications is at its first stages, and many practical aspects still need to be thoroughly investigated.

The goal of the project is to evaluate the potential implementation approaches, and practically investigate the modeling and optimization of RIS-assisted communications. As this multi-objective optimization will have to be performed given limited information about the channel; the adoption of tools from the machine learning field is an attractive option that is being advocated and demonstrated currently.

Conventional understanding regards wireless channels as uncontrollable stochastic links with inherent unreliability. In the past decades, all the effort was made to understand this environment, model it, and combat its unpredictability with sophisticated signal processing techniques (diversity, beamforming, adaptive modulation and coding, …).

In the context of wireless communications evolution towards future 6G networks, the concept of Smart Radio Environments, based on Reconfigurable Intelligent Surfaces (RIS), has been gaining a lot of traction recently, with the aim of being able also to control, at least partially, the wireless channel itself.

The idea of controlling the ambient environment to provide more favorable propagation characteristics represents a paradigm shift. It is not only conceptually interesting but also highly beneficial in a variety of scenarios. Instead of treating reflection and scattering in the environment as uncontrollable phenomena whose effects can only be modeled stochastically, they become part of the system parameters that may be optimized, which can overcome many of the challenges of wireless communications.

Today, the development of RIS for applications in wireless communications is still at early stages; many practical aspects are not well understood and still need to be thoroughly investigated.

The goal of the project is to investigate the different implementation approaches, the channel modeling, and the optimization of RIS-assisted communications. As this multi-objective optimization will have to be performed given limited information about the channel; the adoption of tools from the machine learning field is an attractive option that is being advocated and demonstrated currently.


Involved Partners :

  • IMT Atlantique
  • INPT, Rabat, Morocco
  • UQAM, Montreal, Canada.

Job requirements

Training :

PhD in the one of the following fields or similar: telecommunications, electrical engineering, computer science.


Skills :

  • Wireless Communications
  • Performance Analysis
  • Experience in Machine Learning will be a plus

Benefits

  • 49 days of annual leave and time off
  • Partial telecommuting possible
  • Public transport paid for
  • Sustainable mobility package (for carpooling or cycling)
  • Family supplement
  • Wide range of social benefits

Contact : 

Pr Samir Saoudi, IMT Atlantique, France, samir.saoudi@imt-atlantique.fr

and

Pr Mustapha Benjillali, INPT, Morocco, benjillali@inpt.ac.ma

Application deadline: open until the right candidate is found

Start date : February, 1st 2023


Legal notices

  • Contract : Fixed-term contract
  • Contract duration : 11 months 
  • Location : IMT Atlantique - 655 Av. du Technopôle, 29280 Plouzané
  • The positions offered for recruitment are open to all with, upon request, accommodations for candidates with disabilities.