Machine Learning Based Transceivers

Universities and Institutes of France
December 30, 2022
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Offerd Salary:Negotiation
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Contract Type:Temporary
Working Time:Full time
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  • Organisation/Company: Telecom Paris
  • Research Field: Engineering › Communication engineering Engineering › Electronic engineering
  • Researcher Profile: First Stage Researcher (R1) Recognised Researcher (R2) Established Researcher (R3) Leading Researcher (R4)
  • Application Deadline: 30/12/2022 00:00 - Europe/Athens
  • Location: France › Palaiseau
  • Type Of Contract: Temporary
  • Job Status: Full-time
  • Opening: Nokia bell labs in collaboration with Telecom Paris has a PhD opening for a candidate with an EE background. This PhD position will explore novel digital transceiver designs and apply machine-learning techniques to improve their performance, as well as FPGA implementations in real-time environments. The final goal is an improvement in the energy efficiency of 6G transmitters. Context: The biggest challenge for next-gen communication systems in the upcoming decade is to support a large increase in data consumption while also meeting the energy consumption targets through intelligent usage of the networks. Hyper dense baseband massive MIMO is a part of this vision. This PhD position will explore novel digital transceiver designs and apply machine- learning techniques to improve their performance, as well as FPGA implementations in real-time environments. The final goal is an improvement in the energy efficiency of 6G transmitters

    Contract Duration: 36 months Starting date: Dec 2022 Location: Between Nozay and Palaiseau (Ile de France) To apply: Applicants should submit a cover letter and a detailed CV in a zip file on the following link: https: // partage.imt.fr/index.php/s/zYo7MTyTRii5zkR

    Funding category: Cifre

    PHD title: Doctorat IP Paris

    PHD Country: France

    Offer Requirements Specific Requirements

    Required and desired skills:

  • Good background in the wireless physical layer
  • Strong experience with Python.
  • Knowledge of machine learning techniques
  • Good experience in FPGA design
  • Good understanding of digital-to-analog converter performance & 3GPP standard requirements (ENOB, EVM, ACLR, SEM)
  • Experience with machine learning frameworks (e.g., Tensorflow, PyTorch, etc.)
  • Contact Information
  • Organisation/Company: Telecom Paris
  • Organisation Type: Public Research Institution
  • Country: France
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