PhD Fellowship in AI-Native Networking
Oslo universitetssykehus Se alle jobber
- Oslo
- Fast
- Fulltid
International collaboration network, including partners at Ruhr University Bochum, GermanyContact Information
- Project leader/PI: Dr. Roufaida Laidi - roulai@ous-hf.no
- Prof. Ilangko Balasingham - i.s.balasingham@ous-research
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
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
- 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)
- Machine Learning / Deep Learning (PyTorch or TensorFlow)
- Graph Neural Networks, Reinforcement Learning, Federated Learning, or Network Optimization (at least one)
- Solid Python programming; experience with distributed or networked systems is a plus
- Publication record is an advantage
- 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
- 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
- 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
- A fully funded 3-year PhD position at one of Europe's leading university hospitals
- Salary according to the Norwegian state salary scale (approx. NOK 536,200-587,000/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