Machine Learning

MERL is looking for a self-motivated intern to research on problems at the intersection of multimodal large language models and neural algorithmic reasoning. An ideal intern would be a Ph.D. student with a strong background in machine learning and computer vision. The candidate must have prior experience with training multimodal LLMs for solving vision-and-language tasks. Experience in participating and winning mathematical Olympiads is desired. Publications in theoretical machine learning venues would be a strong plus. The intern is expected to collaborate with researchers in the computer vision team at MERL to develop algorithms and prepare manuscripts for scientific publications.

Required Specific Experience

• Experience with training large vision-and-language models
• Experience with solving mathematical reasoning problems
• Experience with programming in Python using PyTorch
• Enrolled in a PhD program
• Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).

MERL is looking for a self-motivated intern to work on problems at the intersection of multimodal large language models and embodied AI in dynamic indoor environments. The ideal candidate would be a PhD student with a strong background in machine learning and computer vision, as demonstrated by top-tier publications. The candidate must have prior experience in designing synthetic scenes (e.g., 3D games) using popular graphics software, embodied AI, large language models, reinforcement learning, and the use of simulators such as Habitat/SoundSpaces. Hands on experience in using animated 3D human shape models (e.g., SMPL and variants) is desired. The intern is expected to collaborate with researchers in computer vision at MERL to develop algorithms and prepare manuscripts for scientific publications.

Required Specific Experience

• Experience in designing 3D interactive scenes
• Experience with vision based embodied AI using simulators (implementation on real robotic hardware would be a plus).
• Experience training large language models on multimodal data
• Experience with training reinforcement learning algorithms
• Strong foundations in machine learning and programming
• Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).

MERL is seeking interns passionate about robotics to contribute to the development of an Active Simultaneous Localization and Mapping (Active SLAM) framework guided by Large Language Models (LLM). The core objective is to achieve autonomous behavior for mobile robots. The methods will be implemented and evaluated in high performance simulators and (time-permitting) in actual robotic platforms, such as legged and wheeled robots. The expectation at the end of the internship is a publication at a top-tier robotic or computer vision conference and/or journal.

The internship has a flexible start date (Spring/Summer 2025), with a duration of 3-6 months depending on agreed scope and intermediate progress.

Required Specific Experience

• Current/Past Enrollment in a PhD Program in Computer Engineering, Computer Science, Electrical Engineering, Mechanical Engineering, or related field
• Experience with employing and fine-tuning LLM and/or Visual Language Models (VLM) for high-level context-aware planning and navigation
• 2+ years experience with 3D computer vision (e.g., point cloud, voxels, camera pose estimation) and mapping, filter-based methods (e.g., EKF), and in at least some of: motion planning algorithms, factor graphs, control, and optimization
• Excellent programming skills in Python and/or C/C++, with prior knowledge in ROS2 and high-fidelity simulators such as Gazebo, Isaac Lab, and/or Mujoco

Additional Desired Experience

• Prior experience with implementation and/or development of SLAM algorithms on robotic hardware, including acquisition, processing, and fusion of multimodal sensor data such as proprioceptive and exteroceptive sensors