PhD Research Fellow in AI Native Networking for Healthcare

Prof. Ilangko Balasingham; Oslo fylke; On-site; NOK 551,000 - 587,000; Full time

Benefits offered by this job

Fully funded PhD

Collaboration with OUS

High-performance computing access

Job summary

Oslo University Hospital søker en PhD Research Fellow for SYNAPSE-prosjektet i en heltidsstilling. Stillingen er tre år og innebærer forskning i nettverksbasert maskinlæring under veiledning av Prof. Ilangko Balasingham.

Kvalifikasjoner inkluderer master i informatikk eller tilsvarende, solid nettverkskunnskap og erfaring med maskinlæring. Arbeidet foregår ved Intervention Centre og krever bachelor + fem års studier, samt gode programmeringsferdigheter i Python.

Qualifications

• Mastergrad og bachelorgrad som oppgitt i stillingsannonse.
• Sterk faglig bakgrunn i datanettverk og maskinlæring.

Responsibilities

• Utvikle og evaluere læringsbaserte metoder for nettverkskontroll.
• Implementere metoder i simulering og/eller testmiljøer.
• Publisere i ledende tidsskrifter og fullføre PhD ved Universitetet i Oslo.

Skills

Nettverk

Maskinlæring

Python

Teamwork

Kommunikasjon

Education

Mastergrad i informatikk/nettverk/elektronikk

Bachelorgrad 180 ECTS + 5 år samlet utdanning

Tools

ns-3

OMNeT++

Mininet

Job description

We seek a PhD Research Fellow for a 3-year position in the SYNAPSE project. The successful candidate will have a solid background in computer networking and an interest in developing machine-learning methods for network control and network-aware distributed learning.

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 theWireless Sensor Network Research Groupof Professor Ilangko Balasingham and will be enrolled in the PhD program at the University of Oslo

We seek candidates with a solid background in computer networking who want to develop machine-learning methods for network control and network-aware distributed learning.

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 SYNAPSE

SYNAPSE, funded by the Research Council of Norway, develops networks that adapt their decisions to network conditions and the clinical urgency of the information they carry. Learning is integrated into network control to support decisions about scheduling, routing and resource allocation. For example, an urgent clinical alert should reach its destination promptly even when links are congested and patients are moving.

The project also develops distributed and federated learning methods that adapt to connectivity, available resources and clinical priorities. Network control and distributed learning are designed together, so that the network supports learning and learning remains effective when communication is constrained. The methods are evaluated in remote patient monitoring and hospital-at-home scenarios.

This position focuses on network protocols and their control at the MAC, network, transport and cross-layer levels, combined with machine learning. It does not cover hardware, antennas, circuits or physical-layer signal processing, and is not a general data-science position. Applicants must document relevant computer-networking knowledge and experience.

Research areas

• Learning-based network control, including scheduling, link selection, routing and resource allocation in wireless and edge networks.
• Reinforcement learning and multi-agent reinforcement learning for distributed network decision-making.
  
• Graph and hypergraph representation learning of network state, including temporal graph neural networks.
• Network- and urgency-aware distributed and federated learning, adapting the exchange of data and model updates to network conditions and clinical priorities.
  
• Evaluation through network simulation and emulation, and with real remote patient monitoring data.
  

Responsibilities

• Develop and evaluate learning-based methods for urgency-aware network control and network-aware distributed learning.
• Implement and test the methods using network simulation or emulation and real monitoring data.
• Publish in leading networking and machine-learning venues and complete a PhD at the University of Oslo, Faculty of Medicine.

Kvalifikasjoner (overskrift)

Required qualifications

• A master’s degree (120 ECTS), including a thesis, in computer science, network or communication engineering, electrical engineering or equivalent.
• A relevant bachelor’s degree (180 ECTS), with a total of five years of higher education.
• A solid foundation in computer networking, demonstrated through relevant coursework and a substantial project, thesis or research experience involving network protocols, wireless or mobile networks, routing or network performance.
• Coursework or project experience in machine learning, and good programming skills in Python."
• A weighted average grade of B or better on the master’s degree, as required by the University of Oslo.
• A master’s thesis graded B or better (Norwegian grading scale or equivalent).

Preferred qualifications

• Experience with reinforcement learning or graph neural networks.
• Experience with network simulation or emulation tools, such as ns-3, OMNeT++ or Mininet.
• Publications or a master’s thesis combining networking and machine learning.

Language Requirements
Applicants who are not proficient in a Scandinavian language must document English proficiency through one of the following: TOEFL: at least 600 (paper-based) or 92 (internet-based); IELTS Academic: at least 6.5, with no section below 5.5; Cambridge CAE/CPE: grade A or B.

Personal Qualities

• Ability to work independently and collaboratively.
• A structured, precise and adaptable working style.
• Strong communication and teamwork skills.
• A positive attitude and ability to manage a dynamic work environment.
• A high level of professionalism and work ethic.

We offer

• A fully funded, full-time three-year PhD position. Salary according to the Norwegian state salary scale (approximately NOK 550,800–587,000 per year).
• Supervision by Dr Roufaida Laidi (PI), Prof. Ilangko Balasingham and Dr Hemin Ali Qadir, in collaboration with Prof. Tarik Taleb (Ruhr University Bochum).
• A clinical research environment at The Intervention Centre, Oslo University Hospital.
• Access to high-performance computing infrastructure at OUS and national e-infrastructure services.
• An inclusive, interdisciplinary research environment at the Intervention Centre, OUS.
• Pension, insurance and welfare schemes through the Norwegian public sector.
• Family-friendly surroundings with cultural and outdoor opportunities in Oslo.

Your application

• Submit a cover letter of at most one page explaining your interest in SYNAPSE and your fit with the position.

Identify:

• Your computer-networking background, naming relevant coursework, a thesis, substantial projects or publications and explaining your own contribution.
• Your experience implementing machine-learning methods and using network simulation, emulation or testbeds.
• The research area or areas above that best match your background and interests.

Supporting documents

• A CV.
• Transcripts for your bachelor’s and master’s degrees, with the grading scale or diploma supplement.
• Copies of your diplomas.
• Documented English proficiency.
• Names and contact details of two referees, at least one academic. Referees must not be family members.
• Documents not in English or a Scandinavian language must be accompanied by a certified translation. Applications without transcripts and diplomas will not be assessed.

Kontaktinformasjon

Dr. Roufaida Laidi,Project leader, PI,[e-mail]
Prof. Ilangko Balasingham,Section leader,[e-mail]

Arbeidssted

Sognsvannsveien 20
0372Oslo

Nøkkelinformasjon:

Arbeidsgiver: Oslo universitetssykehus HF
Referansenr.: 5199969633
Stillingsprosent: 100%
Engasjement
Startdato: 01.01.2027
Sluttdato: 31.12.2029
Søknadsfrist: 15.11.2026