PhD Fellowship in AI-native Networks

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.

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

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 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:

• Strong background in:

Preferred Qualifications:

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:

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

KontaktinformasjonDr. Roufaida Laidi, Project leader/PI, roufaida.laidi@ous-hf.no

Dr. Ilangko Balasingham, Head of Section, Professor, ilangko.balasingham@ous-research.no

ArbeidsstedSognsvannsveien 20

0372 Oslo

Nøkkelinformasjon:Arbeidsgiver: Oslo universitetssykehus HF

Referansenr.: 5169482980

Stillingsprosent: 100%

Engasjement

Søknadsfrist: 13.09.2026