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Field
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computation is necessary before taking an action. This PhD project will address these challenges by developing trustworthy and resource-adaptive VLA models. A central question is whether an embodied agent can
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-aware reference monitor on top of seL4, supporting dynamic control of the capabilities and information flows of AI agents. Develop and mechanise security models, program logics and information-flow
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-quality agents, predictive models that can transfer across machine families, prescriptive decision support, operational dashboards, and ensuring the delivery of methodologies, documentation, training, and
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agentic systems. This PhD research will contribute to the field by: Autonomous multi-agent systems for real-world decision-making Agentic LLMs with planning, tool use, and self-evaluation capabilities
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theory and/or agent-based models) to formalize strategic interactions involving algorithmic pricing technologies. • Empirical analysis of real-world pricing data (e.g. e-commerce platforms) to detect
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when using closed, proprietary models, where model weights, training data, and internal representations are inaccessible. The PhD project will therefore investigate how trustworthy agentic AI systems can
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their orchestration, provenance, and evaluation. Experience integrating AI/ML models and services with HPC and workflow systems, such as inference serving on leadership systems, coupling of AI agents with simulation
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learning-based model predictive control (MPC) algorithms for multi-agent multirotor drone navigation around vessels in maritime environments. The role will focus on integrating multirotor crash predictions
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analytics, or network analysis. • Experience in agent-based modeling or simulation is highly desirable. • Demonstrated ability to publish in internationally recognized peer-reviewed journals
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Engineering. DCSC is an internationally leading research centre where PhD candidates, postdocs and faculty members work on modeling, analysis, control and optimization of complex dynamical systems relevant