Tarik Taleb

Oslo University Hospital invites applications for a fully funded three-year PhD Research Fellowship in AI-Native Networking for Healthcare within the SYNAPSE project, funded by the Research Council of Norway. The project develops network control and distributed learning methods for healthcare scenarios such as remote patient monitoring and hospital-at-home. The research is centered on adaptive networking, where scheduling, routing, and resource allocation respond to network conditions and clinical urgency. It also explores network-aware distributed and federated learning that adapts to connectivity, available resources, and patient priorities. The position sits in the Wireless Sensor Network Research Group at the Intervention Centre, Oslo University Hospital, and the successful candidate will complete a PhD at the University of Oslo, Faculty of Medicine . Research topics include reinforcement learning, multi-agent reinforcement learning, graph and hypergraph representation learning, temporal graph neural networks, and network-aware federated learning. This is a networking-focused PhD: it covers network protocols and control at the MAC, network, transport, and cross-layer levels. It explicitly does not focus on hardware, antennas, circuits, physical-layer signal processing, or general data science. The candidate will develop and evaluate methods using network simulation or emulation and real monitoring data, publish research findings, and contribute to an interdisciplinary clinical research environment with international collaboration. Funding: the fellowship is fully funded and full-time for three years. Salary follows the Norwegian state salary scale, approximately NOK 550,800–587,000 per year, and benefits include pension, insurance, welfare schemes, and access to high-performance computing infrastructure. Eligibility highlights: applicants should hold a relevant master’s degree (120 ECTS) with thesis in computer science, network or communication engineering, electrical engineering, or equivalent, plus a relevant bachelor’s degree (180 ECTS), with five years of higher education in total. A master’s weighted average of B or better and a master’s thesis graded B or better are required. Good English is required, and applicants should be prepared to demonstrate networking strength, machine-learning exposure, and Python skills. Preferred experience includes reinforcement learning, graph neural networks, ns-3, OMNeT++, Mininet, and prior work combining networking and machine learning. Application window: apply by 15 November 2026 via Webcruiter. Submit a cover letter (max one page), CV, bachelor’s and master’s transcripts with grading scales, diplomas, documented English proficiency where required, and contact details for two referees, including at least one academic referee. Certified translations are required for documents not in English or a Scandinavian language.