Your work will focus on how large-scale AI models can acquire robust, generalizable, and goal-directed behaviors through reinforcement learning, multimodal experience, and interaction with learned or simulated environments. In this context, you will investigate how predictive models of environment dynamics, latent state, and agent-environment interaction can support policy learning, planning, behavior synthesis, and evaluation. o excellent MSc in Computer Science, Machine Learning, Robotics, Control, or related technical fields + training large-scale behavior or policy models from multimodal data and interaction + linking large-model training with policy learning and environment interaction
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