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PhD Position in Cloud-Native and AI-Native 6G Networks

University of Amsterdam Faculty of Science
✓ Fully Funded ⏰ Closing Soon 🎓 Computer Science 🎓 Electrical Engineering programmable networking 6g networks cloud-native ai-native radio access networks data plane programming network slicing network automation

Explore programmable data planes in 6G with a focus on cloud-native and AI-native Radio Access Networks. Investigate abstraction layers and intelligent network management in collaboration with Ericsson, advancing scalable and flexible future mobile networks.

AI-generated overview

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Why This Research Matters

This research advances the foundational technologies essential for the future of 6G networks, enabling efficient and intelligent management of network resources. Its outcomes will facilitate improved scalability, flexibility, and automation in mobile communications, fostering innovative applications and services across industries globally.

Cloud-Native Networking AI-Native Networks Data-Plane Programmability Radio Access Networks Network Slicing 6G Communications

Project Description

Project Overview

Software-defined and programmable networking is key to cloud-native and AI-native 6G networks, enabling scalability, flexibility, automation, and efficient resource use. This project investigates data-plane programmability in future mobile networks, focusing on common abstraction layers for heterogeneous programmable data planes, data-plane multitenancy, modularization, and programmable radio resource management with intelligent control. Conducted in collaboration with Ericsson and embedded in the MNS group, the research supports the Dutch 6G flagship project involving leading ICT businesses and research institutions.

What You Will Do

  • Develop and evaluate an end-to-end programmable data plane supporting multitenancy and modularity with a generic hardware abstraction layer for systems like GPUs, TPUs, FPGAs, and SOCs.
  • Investigate optimization of network control and data plane functions.
  • Develop proof of concepts to benchmark performance.
  • Utilize state-of-the-art testbeds and engage in the Dutch 6G ecosystem.
  • Collaborate with international research teams, publish in conferences and journals, and contribute to teaching and student supervision.

Expected Outcomes

Innovations in data-plane programmability for 6G networks, creating flexible, intelligent network architectures that improve resource management, scalability, and automation in future mobile communication systems.

Why This Matters

6G networks will require unprecedented levels of programmability and intelligence across network layers to support diverse applications and services. This research will contribute to foundational technologies enabling efficient, scalable, and adaptive mobile networks, impacting telecommunications globally.

Entry Requirements

Master's degree or equivalent in computer science, electrical and computer engineering, security and network engineering, or related field. Excellent programming skills (Python, C/C++). Experience with cloud-native platforms (Kubernetes) and programmable networking technologies (eBPF, P4) is advantageous. Knowledge of mobile network architectures is a plus. Fluency in English and good presentation skills required.

Eligibility

UK/Home
EU
International

Supervisor Profile

DC
Dr. Chrysa Papagianni
University of Amsterdam, Faculty of Science

Dr. Chrysa Papagianni is a researcher specializing in programmable networks and next-generation wireless communication technologies. Her work focuses on developing flexible, scalable network architectures, with expertise in cloud-native and AI-driven mobile network paradigms. She leads projects at the University of Amsterdam and collaborates with industry leaders like Ericsson to bridge academic research with practical 6G network innovations.

Key Publications

2013 306 citations
On the optimal allocation of virtual resources in cloud computing networks
2013 129 citations
A cloud-oriented content delivery network paradigm: Modeling and assessment
2021 105 citations
End-to-end intent-based networking
2012 96 citations
Efficient resource mapping framework over networked clouds via iterated local search-based request partitioning
2016 93 citations
Mobile crowdsensing as a service: a platform for applications on top of sensing clouds

Research Contributions

Developed optimal allocation strategies for virtual resources in cloud computing networks to improve efficiency.
Enhanced resource utilization and performance in cloud computing infrastructures.
Proposed a cloud-oriented content delivery network paradigm with modeling and assessment.
Improved content delivery mechanisms in cloud environments with enhanced security and dependability.
Advanced the concept of intent-based networking through an end-to-end framework.
Enabled more intuitive and dynamic network management and automation.
Created efficient resource mapping frameworks using iterated local search for networked clouds.
Optimized service allocation and resource management in distributed cloud systems.

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