GARAH Abdelhamid
Top university
3 days ago
PhD in Quantum Machine Learning for Intrusion Detection in 5G/6G Networks Université Paris-Saclay in France
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Deadline
Sep 30, 2026
Country
France
University
Université Paris-Saclay

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About this position
PhD opportunity in Quantum Machine Learning for intrusion detection in 5G/6G traffic at UVSQ, Université Paris-Saclay (DAVID Laboratory, NGN Team), Versailles, France.
This PhD focuses on building a QML-based Intrusion Detection System (QML-IDS) for 3GPP 6G traffic and cloud-native 5G/6G core networks. The research combines Computer Science, Electrical Engineering, Information Technology, Mathematics, Cybersecurity, Machine Learning, and Quantum Machine Learning. The project addresses three main challenges: integrating intrusion detection into the 5G/6G Service-Based Architecture in compliance with 3GPP specifications; implementing and testing the system on the NGN team’s 5G core testbed using tools such as PennyLane, Qiskit, and Cirq; and designing and benchmarking QML detection algorithms for CIoT, eMBB, URLLC, V2X, and future 6G traffic classes.
Eligibility highlights: a Master’s degree in Computer Science obtained in France or another European country, a completed Master’s internship in France or Europe, strong knowledge or experience in 5G/6G networks, a strong academic record, and strong motivation for research and an academic career. Helpful extras include ML for networking/cybersecurity and familiarity with PennyLane, Qiskit, or Cirq.
Application: submit by email only before 2026-09-30. Use the subject line [PhD QML-IDS 6G] and send a single PDF containing the CV, motivation letter, Bachelor’s and Master’s transcripts, academic ranking and cohort size, and recommendation letters.
Funding details
Available
What's required
Applicants must hold a Master's degree in Computer Science obtained in France or another European country, and must have completed a Master's internship in France or Europe. Strong knowledge or experience in 5G/6G networks is required, along with a strong academic record and strong motivation for research and an academic career. Expected knowledge includes computer networks and telecommunications, machine learning/artificial intelligence, programming and software development, applied mathematics, and quantum machine learning. Additional assets include experience with machine learning applied to networking or cybersecurity and familiarity with PennyLane, Qiskit, or Cirq.
How to apply
Apply by email only with the subject line "[PhD QML-IDS 6G]". Send a single PDF containing the CV, motivation letter, transcripts, academic ranking and cohort size, and recommendation letters.
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