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. + experience with model-based RL, offline RL, hierarchical RL, multi-agent RL, or constrained RL is highly desirable + familiarity with structured engineering artifacts such as requirements, system models, dependency graphs, simulation outputs, or test specifications
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