This includes developing planning, reasoning, navigation and exploration algorithms (e.g., efficient RL, imitation learning approaches like behavioral cloning, game theoretic equilibriums, hierarchical controllers, causal and probabilistic reasoning modules) that ensure trustworthy behavior of agents in autonomous laboratory environments and enable long-horizon, multi-step high-throughput experimental campaigns. Experience or strong interest in developing AI/ML algorithms and methods for Single-Agent and Multi-Agent Autonomous Decision Making (such as reinforcement learning, imitation learning, game theory, control algorithms, large multimodal models (VLMs/VLAs), or Bayesian optimization) to autonomously explore physical scientific discovery, laboratory automation, or experimental control.