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locations, with priority determined according to the order of listing.\nWhat you’ll do\nAs a PhD intern, you will:\nCollaborate with research scientists to advance methods in:\nPlanning and RL for computer use (e.g. behavioral cloning, RL on model weights, RAG-based domain knowledge)\n\nMultimodal grounding (e.g. vision-only models, tree search, hybrid methods with large models)\n\nReward/judge modeling (e.g. error analysis, human evaluation, training judge models)\n\nUser intent understanding (e.g. modeling vague queries, preference learning)\n\nContribute to building datasets, running experiments, and benchmarking results\n\nExplore novel approaches and help derisk Simular’s long-term technical roadmap\n\nDocument and communicate findings through internal reports or academic-style writing\n\nYou might be a fit if\nCurrently pursuing a PhD in Computer Science, Machine Learning, or related field\n\nResearch background in at least one of: Reinforcement learning, Large 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