This includes leveraging world-model-based rollouts for scalable training, using imagined trajectories for efficient policy improvement, and developing validation frameworks that assess generalization, robustness, and safety before real-world deployment. o excellent MSc in Computer Science, Machine Learning, Robotics, Control, or related technical fields + strong interest in behavior validation, robustness testing, sim-to-real, and safety + experience with latent-state modeling, uncertainty-aware prediction, and validation
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