The Machine Learning and Language (MLL) Lab at Northwestern University, led by Assistant Professor Manling Li, is recruiting PhD students and postdoctoral researchers for Fall 2026. The lab is affiliated with the Center for Robotics and Biosystems and focuses on cutting-edge research at the intersection of machine learning, language models, robotics, embodied agents, and artificial intelligence. Research topics include reasoning, planning, agent training, spatial intelligence, multimodal reasoning, and the development of safe and trustworthy AI systems. MLL Lab's research agenda covers: Reasoning, Planning, and Agent Training: Developing embodied agent interfaces, reinforcement learning agents, and world modeling for intelligent systems. Spatial Intelligence and Embodied Agents: Advancing spatial reasoning, multimodal alignment, and the integration of language and vision for robotics and AI agents. Mechanistic Interpretability: Opening up AI models to understand their inner workings and improve transparency. Safety and Trustworthiness: Ensuring AI systems are safe, reliable, and interpretable through innovative planning and control methods. The lab has achieved significant recognition in its first year, including multiple best paper and poster awards at top conferences (ACL, NeurIPS, ICML, RSS, ICCV), invited talks at leading institutions, and prestigious fellowships. The group is highly collaborative and values diversity, creativity, and student-driven research directions. Applicants should have a strong background in computer science, AI, machine learning, robotics, or related fields. Experience with multimodal reasoning, embodied agents, or spatial intelligence is a plus. The lab is fully funded, and PhD students receive tuition coverage and a stipend. The application deadline for Fall 2026 is December 28, 2025. Prospective students should apply to the Northwestern Computer Science PhD program and select Manling Li as a potential advisor. For research internships, submit the form on the lab website and email [email protected]. For more information, visit the lab website: https://limanling.github.io/