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. + familiarity with agentic AI architectures that involve goal-directed behavior, memory, tool use, multi-step reasoning, and long-horizon task execution + strong interest in applying AI to systems engineering tasks such as design-space exploration, requirement analysis, architecture optimization, verification support, or engineering workflow automation
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