Research Associate in Edge and Cloud Computing: Deployment and Resource Management

We are a world class research-intensive university. We deliver teaching and learning of the highest quality. We play a leading role in economic, social and cultural development of the North East of England. Attracting and retaining high-calibre people is fundamental to our continued success.

The Role
The National EdgeAI Hub is one of the nine UK hubs funded by UKRI’s AI for Real Data initiative, addressing key challenges in cybersecurity, data quality, and AI trustworthiness in edge computing environments. The hub includes collaborations with 12 UK universities and over 50 industrial partners, driving innovation in AI for real-world applications across healthcare, smart cities, energy, and autonomous transport. For more information about the hub’s mission and objectives, please visit here.

This position will be responsible for developing and optimizing methodologies for cloud scheduling, resource orchestration, multi-objective optimization, and deployment in edge computing and cyber-physical systems (CPS). The role involves designing efficient scheduling algorithms, improving workload distribution strategies, and optimizing cloud-edge resource allocation to enhance system performance, and resilience. The successful candidate will bring expertise in distributed computing, and cloud-edge resource management, with a strong emphasis on developing scalable and secure deployment strategies. Additionally, the successful candidate will be expected to contribute to research publications, collaborate with industrial and academic partners, and support the broader objectives of the National EdgeAI Hub.

This project requires expertise in multi-objective optimization, cloud-edge resource orchestration, and secure deployment strategies for Edge computing applications, particularly in energy and transport sectors. A strong understanding of optimization techniques, edge computing, and workload balancing is essential to develop resilient and efficient cloud-edge frameworks for CPS. The research will contribute to advancing secure and adaptive scheduling mechanisms, optimizing computational resources, and facilitating the safe deployment of edge-based services in critical infrastructure.

As part of our commitment to career development for research colleagues, the University has developed 3 levels of research role profiles. These profiles set out firstly the generic competences and responsibilities expected of role holders at each level and secondly the general qualifications and experiences needed for entry at a particular level.

This role is fixed-term for 12 months and is part-time at 18.5 hours per week (50% FTE).

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