MERL is looking for a highly motivated individual to work on long-horizon planning and decision making for robotic manipulation using large language models (LLMs) and Hierarchical RL. The research will develop novel algorithms for long-horizon task planning and execution using Hierarchical Reinforcement learning and LLMs. The ideal candidate should have experience in either one or multiple of the following topics: (Deep) Reinforcement learning, Hierarchical RL, LLMs, policy optimization and Markov Decision Processes (MDPs). Senior PhD students in machine learning and engineering with a focus on Reinforcement Learning and robotics are encouraged to apply. Prior experience working with physics engines like Mujoco, Isaac Gym, etc. is required. Prior experience working with Python and ROS is required. A successful internship will result in submission of results to peer-reviewed conference and journals. Good coding skills in Python and state-of-the-art RL environments (e.g., RL Bench) is required. The expected duration of internship is 3-4 months with flexible start dates. This internship is preferred to be onsite at MERL.