PhD in Quantum Machine Learning for Intrusion Detection in 5G/6G Networks
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.
