PhD Fellowship in AI-native Networks

Om stillingen

About the research group
Section for Medical Information and Communication Technology (Medical ICT Research), The Intervention Centre (see: http://www.ivs.no), at Oslo University Hospital offers a full-time PhD Fellowship. The recruited PhD Fellow will be part of the Wireless Sensor Network Research Group of Professor Ilangko Balasingham and will be enrolled in the PhD program at the University of Oslo

Oslo University Hospital is a workplace with great diversity. We believe this is crucial for solving our tasks in the best possible way. We want this diversity to be reflected among the applicants for our positions, and we encourage all qualified candidates, regardless of background, to apply!

The fellowship is part of the SYNAPSE (SYnergetic Network-AI Platform for Semantic Efficiency) project, funded by the Research Council of Norway (12 MNOK).

SYNAPSE addresses a critical gap in next-generation networks: the disconnect between distributed AI processes and network infrastructure. The project develops an AI-native ecosystem where communication and computation co-evolve through cross-layer awareness, integrating semantic hypergraph intelligence, urgency-weighted federated learning, and multi-agent reinforcement learning for mission-critical healthcare applications. The framework is validated through remote patient monitoring scenarios, targeting substantial improvements in latency, reliability, and energy efficiency.

For more information, visit the SYNAPSE project page at: https://www.linkedin.com/company/synapse-ous
Supervision by Dr. Roufaida Laidi (PI), Prof. Ilangko Balasingham, and Dr. Hemin Qadir

International collaboration network, including partners at Ruhr University Bochum, Germany. 

Contact Information
Project leader/PI: Dr. Roufaida Laidi – [email protected]
Prof. Ilangko Balasingham – i.s.balasingham@ous-research

Oslo University Hospital is a workplace with great diversity. We believe this is crucial for solving our tasks in the best possible way. We want this diversity to be reflected among the applicants for our positions, and we encourage all qualified candidates, regardless of background, to apply!

Arbeidsoppgaver

• About the PhD Project
  
  The PhD fellow will be a core member of the SYNAPSE team and will focus primarily on Work Package 1 (Semantic Hypergraph Modeling and Dynamic Link Control), with contributions to Work Package 3 (Cross-Layer MARL Orchestration). The research will include:
  

  Designing and formalizing semantic hypergraph representations that encode urgency propagation paths, device relationships, and resource constraints in distributed networks

  Developing and training temporal Graph Neural Networks (GNNs) to produce unified embeddings capturing both network dynamics and semantic priorities

  Integrating hypergraph embeddings into multi-agent reinforcement learning frameworks to enable coordinated, real-time link control decisions
  

  Implementing decentralized link adaptation mechanisms for predictive rerouting and prioritization of critical data flows
  

  Validating the framework under realistic conditions including mobility, congestion, and heterogeneous workloads

  ability to solve complex challenges, and we encourage all qualified candidates to apply regardless of background.
  
  

  Responsibilities
  

  The PhD candidate will:
  

  Conduct research in AI-native networking and semantic hypergraph intelligence for healthcare applications
  

  Design and implement graph neural network models and multi-agent reinforcement learning frameworks for cross-layer network orchestration
  

  Validate research outcomes under realistic network conditions including mobility, congestion, and heterogeneous workloads Contribute to scientific publications in top-tier venues (e.g., NeurIPS, IEEE Transactions on Networking, ACM Internet Technology) and collaborative research activitie    

Kvalifikasjoner (overskrift)

Required Qualifications:

• MSc degree in Computer Science, Electrical Engineering, or a related field (120 ECTS), including a thesis
• BSc degree (180 ECTS)
• Strong academic performance (minimum grade B; A preferred)
• Master’s thesis graded B or better (Norwegian system or equivalent)
• Strong background in:
  

1. Machine Learning / Deep Learning (PyTorch or TensorFlow)
2. Graph Neural Networks, Reinforcement Learning, Federated Learning, or Network Optimization (at least one)
3. Solid Python programming; experience with distributed or networked systems is a plus
4. Publication record is an advantage
   

 Preferred Qualifications:

• Experience with semantic communication, network simulation, or software-defined networking
• Familiarity with hypergraph or higher-order network models
• Publications in relevant peer-reviewed venues
• Interest in healthcare AI applications and interdisciplinary collaboration
• Experience with HPC environments and large-scale experiments
  

Language Requirements:

Applicants who are not proficient in a Scandinavian language must document English proficiency through one of the following:

TOEFL: ≥ 600 (paper-based) or ≥ 92 (internet-based)

IELTS (Academic): ≥ 6.5 (no section below 5.5)

Cambridge CAE/CPE: Grade A or B   

     Personal Qualities:

• Ability to work independently and collaboratively

  Structured, precise, and adaptable working style
  

  Strong communication and teamwork skills
  

  Positive attitude and ability to manage a dynamic work environment
  

  High level of professionalism and work ethic

We Offer

• A fully funded 3-year PhD position at one of Europe’s leading university hospital
• Salary according to the Norwegian state salary scale (approx. NOK 550,800–587,000/year)
• Access to high-performance computing infrastructure at OUS and national e-infrastructure services
• An inclusive, interdisciplinary research environment at the Intervention Centre, OUS
• Generous benefits including pension, insurance, and welfare schemes through the Norwegian public sector
• Family-friendly surroundings with excellent cultural and outdoor opportunities in Oslo 

Kontaktinformasjon

Arbeidssted

Nøkkelinformasjon: