Your work will address how intelligent agents can support and partially automate complex engineering workflows by learning to make structured decisions in environments shaped by constraints, specifications, system models, and long-horizon objectives. You will investigate how reinforcement learning, hierarchical decision-making, model-based methods, and planning can be combined with modern agentic AI architectures to support engineering tasks such as architecture exploration, requirement analysis, system-level trade-off evaluation, validation support, and process optimization. Your work may also involve the interaction between language-based agents and formal engineering tools, enabling AI systems that can operate across textual, symbolic, and numerical representations. + experience with model-based RL, offline RL, hierarchical RL, multi-agent RL, or constrained RL is highly desirable
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