This is an exceptional opportunity for a PhD candidate to shape the future of space-mission design through AI. The project focuses on building and evaluating a generative-AI requirements regulatory compliance solution that helps translate complex mission briefs and regulations into rigorous, traceable, regulator-ready requirements. The candidate will join an interdisciplinary community at Monash University spanning computing, systems engineering and socio-technical research, and will engage with government and industry stakeholders under confidentiality arrangements. You will contribute to open, reproducible methods and public guidance for responsible AI in high-assurance settings, particularly in space domain. We invite applications from outstanding PhD candidates with undergraduate or postgraduate qualifications in software/systems engineering, or computer science. Applicants with training in quantitative and empirical research and experience in requirements engineering, safety-critical systems, or AI/ML/LLMs/Knowledge Graphs are especially encouraged to apply. This PhD forms part of a research program at Monash University focused on advancing requirements engineering for space missions. The project investigates how mission briefs, system models, and regulatory texts can be translated into rigorous, auditable requirements—particularly when AI-assisted methods are introduced. Working with de-identified data, the candidate will study real development workflows while contributing open, reproducible techniques suitable for high-assurance contexts. The project will generate new knowledge about RE in the space sector, produce validated techniques to improve traceability and impact analysis, and offer practical guidance for procurement, assurance, and regulation. Benefits include enhanced efficiency in early design, stronger compliance pathways, and improved accountability for AI-supported engineering. Prior experience with space systems or specific companies is not required; we seek a researcher eager to apply rigorous knowledge of LLMs/Knowledge Graphs/ AI methods to this domain.