Researcher (m/f/x) in Distributed Intelligence and Learning Across the Cloud-Edge Continuum

The Institute of Networked Energy-Efficient Systems is dedicated to advancing autonomous, energy-efficient cloud-based networks that support cutting-edge services and innovations. We are assembling a talented international team committed to pioneering research and teaching in this dynamic field.

We are looking for a highly motivated researcher (m/f/x) to explore how intelligence itself can be distributed, coordinated, and optimized across devices, edge nodes, and cloud infrastructures. The position addresses the fundamental question of how learning and inference should be embedded inside future networked systems. The researcher will develop novel paradigms for distributed, federated, and continual learning tailored to highly dynamic, resource-constrained, and privacy-sensitive environments. The work aims to tightly integrate learning processes with network and system behavior. The researcher (m/f/x) will join the Institute of Networked Energy-Efficient Systems at the Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum.

Scope: full-time
Duration: fixed-term, 3 years
Start: at the earliest possible date
Apply by: 2026-03-23

Your tasks:

· Research and Development:

o Design and evaluate distributed learning algorithms that operate across heterogeneous nodes, exploring model splitting, collaborative inference, and adaptive training strategies.

o Investigate privacy-preserving and energy-efficient learning mechanisms and study the co-design of AI models and network architectures

o Prototype and experimentally validate the proposed solutions on edge-cloud platforms and mobile network infrastructures, with results published in top-tier venues at the intersection of AI systems and networking.

· Collaboration: Work collaboratively with cross-functional teams, including hardware engineers (m/f/x), software developers (m/f/x), and other researchers (m/f/x) to integrate AI solutions with network and cloud infrastructure.

· Publication and Presentation: Publish research findings in high-impact journals and present at international conferences and symposiums to disseminate knowledge and advance the field.

· Project Management: Assist in the management of research projects, including the preparation of reports, grant proposals, and progress updates.

· Teaching Assistance: Help with teaching courses related to clouds and networks, contributing to curriculum development and providing support to students (m/f/x).

· Event Organization: Support the organization of online webinars, events, workshops, and conferences related to the Chair's activities.

Your profile:

· Education:

o above-average Master’s degree in machine learning, computer science, or a related field.

o initial experience in publications in renowned transactions, journals, and the proceedings of reputable conferences are desirable

· Technical Skills:

o Strong background in machine learning and AI methodologies, distributed or federated learning

o Good analytical skills and expertise in programming languages (Python, C/C++, Java), AI/ML frameworks (e.g., PyTorch, JAX), network simulators (e.g., ns-3, OMNeT++), and hardware development (e.g., FPGA, embedded systems).

o Knowledge of 5G and beyond technologies, edge computing, etc is highly desirable.

o Ability to independently design, execute, and analyse experimental research.

· Soft Skills:

o Excellent problem-solving skills and analytical thinking.

o Strong written and verbal communication skills in English.

o Ability to work both independently and as part of a multidisciplinary team.

· Preferred Qualifications:

o Experience in real-time systems and edge computing.

o Familiarity with AI-driven network management and orchestration.

o Knowledge of security and privacy issues in wireless communications.

[https://jobs.ruhr-uni-bochum.de/jobposting/35612a99aba6d0b5278b13ed6b47072c54b2f0e80?ref=AfA](https://jobs.ruhr-uni-bochum.de/jobposting/35612a99aba6d0b5278b13ed6b47072c54b2f0e80?ref=AfA)