Doctoral student in Computer Science: Secure Distributed AI
About this role
Örebro University and the School of Science and Technology are looking for a doctoral student (PhD student) for the doctoral programme in Computer Science, concluding with a doctoral degree.
Start date: 1st of May 2026
Project description
The multi-disciplinary centre for cyber resilient AI, RESIST, is a national effort funded by the Swedish Strategic Research Foundation (SSF) to bring together leading researchers in AI and cybersecurity to develop novel solutions to cyber resilient AI for the benefit of Swedish industry and society. The vision is to make Sweden a role model in secure trustworthy AI by pioneering cyber resilience across the AI lifecycle. The research program focuses on four key themes: Trustworthy and Verifiable AI, Runtime Security Assurance, Robust and Secure AI-Supported Development, and Resilient Distributed and Agentic AI. RESIST will drive world-class research in the intersection between AI and cybersecurity through a strong, stimulating and well connected international research environment. Research outcomes will be validated in realworld scenarios with industry and public-sector partners. RESIST will also serve as a national hub for cyber resilient AI, promoting education, knowledge sharing, and policy development.
This PhD position is associated with the research theme Resilient Distributed and Agentic AI.
For systems with multiple agents, not only must the internal learning and reasoning of each agent function effectively, but the interactions between agents, as well as between agents and their environment, must also be safeguarded. That means it must be protected against malicious manipulation, unintended information leakage, protocol deviations, and unsafe or non-compliant behaviors that may compromise system integrity, safety, or performance. In this project, the research done by the PhD student will address the interaction layer in a multi-agent systems. It will focus on how to secure interactions can be generated based on emerg