Internship: Digital Twins for Radio Access Network in 5G and Beyond

Job title:

Internship: Digital Twins for Radio Access Network in 5G and Beyond

Company

Ericsson

Job description

Job Description:Internship: Digital Twins for Radio Access Network in 5G and Beyond
Contacts: anh.khoa.dang@ericsson.com, hicham.khalife@ericsson.com,
christina.giraud@ericsson.com
Final year engineering School or Master 2 level
Duration: 6 months
Starting Date: February 2025
Location: Ericsson R&D center Massy-FranceEricsson’s new R&D center in Massy, France, is covering many cutting-edge technologies such as AI/ML, cloud and 5G/6G technologies. Within this new lab, the Standards & Technology unit is part of the Development Unit Networks’ global Standards & Technology organization. At Development Unit Networks, Standards & Technology secures technology leadership in Radio Access Networks (RANs) by actively driving New Concepts, Standardization, SW and HW Research, Architecture and Testbeds.As a part of this talented research team, we are looking for the next year internships that will help us grow and support our activities. In particular, we are focusing on RAN automation using AI and ML, an area that has seen significant efforts in recent years. However, deploying and validating RAN automation solutions in a live network poses substantial financial, operational, and technical challenges. To address these challenges, we are exploring an alternative approach by building a synthetic RAN environment that reflects real-world conditions, allowing us to conduct thorough testing and validation. The selected candidate will contribute to the development of a high-fidelity simulation environment that closely mimics real-world RAN behavior. This simulated environment will act as a sandbox for testing, refining, and validating algorithms under various realistic scenarios, without the high costs and risks of live deployment.More specifically, the candidate will first investigate and analyze the available real-world datasets collected from live networks to gain a better understanding of the actual environment. Next, he/she will research several internal simulation tools with the goal of integrating these datasets to develop a digital twin that emulates the characteristics of a realistic RAN environment. Additionally, the simulation will need to expose APIs for implementing actions from Reinforcement Learning paradigm. Subsequently, the candidate will incorporate our developed RAN automation solutions into their developed digital twin for testing and validation.

To be successful in the role you need to have
  • Good knowledge of telecommunications (especially Radio Access Network),
  • Good knowledge of cloud architectures (preferably AWS) and cloud native environments,
  • Very good knowledge of programming language (C++, MATLAB, Python),
  • Very good knowledge of Statistics, Machine Learning and notions of Reinforcement Learning,
  • Fluent English language skills in both writing and conversation.

Interns:Interns who join us will enjoy an outstanding chance to make connections, to make change, to make a real difference. We are always looking for interns to become permanent members of our Ericsson team, so you’ll have the opportunity to discuss your future with us during your internship.What happens once you apply?to find all you need to know about what our typical hiring process looks like. This may vary depending on the location and role.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that’s why we nurture it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team.
Ericsson is proud to be an Equal Opportunity and Affirmative Action employer,Primary country and city: France (FR) || MassyReq ID: 758092

Expected salary

Location

Massy, Essonne – Palaiseau, Essonne

Job date

Fri, 29 Nov 2024 23:52:51 GMT

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