Machine Learning

MERL is seeking a highly motivated and qualified intern to conduct research on applying foundation models to robotic manipulation. The focus will be on leveraging large-scale pretrained models (e.g., vision-language models, multimodal transformers, diffusion policies) to enable generalist manipulation capabilities across diverse objects, tasks and embodiments including humanoids. Potential research topics include few-shot policy learning, multimodal grounding of multiple sensor modalities to robot actions, and adapting foundation models for precise control and high success rate.

The ideal candidate will be a senior Ph.D. student with a strong background in machine learning for robotics, particularly in areas such as foundation models, imitation learning, reinforcement learning, and multimodal perception. Knowledge on large-scale Vision-Language-Action (VLA) and multimodal foundation models is expected. The internship will involve algorithm design, model fine-tuning, simulation experiments, and deployment on physical robot platforms equipped with cameras, tactile sensors, and force/torque sensors. The successful candidate will collaborate closely with MERL researchers, with the expectation of publishing in top-tier robotics or AI conferences/journals. Interested candidates should apply with an updated CV and relevant publications.

Required Specific Experience

• Strong background in machine learning for robotics, particularly foundation models (e.g., pi_0, OpenVLA, RT-X, etc.) and imitation learning.

• Experience with simulation environments such as Mujoco, Isaac Gym, or RLBench.

• Experience with physical robot platforms and sensors (vision, tactile, force/torque).

• Proficiency in Python, PyTorch, and modern deep learning frameworks

• Strong publication record in robotics, machine learning, or AI venues

Internship Details

• Duration: ~3 months
• Start Date: Fall 2025 (flexible based on mutual agreement)
• Goal: Publish research at leading robotics/AI conferences and journals

The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research on sensor reasoning models—algorithms that can understand, explain, and act on multi-sensor data (e.g., RF, infrared, LiDAR, event camera) through text-, visual-, and multimodal reasoning. Ideal candidates will be comfortable bridging modern perception (detection/segmentation/tracking) with higher-level reasoning capabilities. Experience with text, visual, and multimodal reasoning is highly preferred. The intern will work closely with MERL researchers to develop novel algorithms, design experiments using MERL’s in-house testbeds, and prepare results for patents and publication. The internship is expected to last 3 months, with a flexible start date from October 2025 onward.

Required Specific Experience

• Reasoning with sensor data: Demonstrated work in text-, visual-, and multimodal reasoning (e.g., VQA over sensor streams, temporal/spatio-temporal reasoning, chain-of-thought, instruction following).
• LLMs & VLMs for sensor perception: Experience aligning or conditioning LLMs/VLMs on sensor outputs (e.g., point clouds, radar heatmaps, BEV features).
• Perception foundations: Solid understanding of state-of-the-art transformer-based (e.g., DETR) and diffusion-based (e.g., DiffusionDet) frameworks
• Datasets & evaluation: Hands-on experience with open large-scale multi-sensor datasets (e.g., nuScenes, Waymo Open Dataset, Argoverse) and open radar datasets (e.g., MMVR, HIBER, RT-Pose, K-Radar). Ability to design reasoning-centric benchmarks (e.g., QA over multi-sensor inputs, temporal prediction).
• Proficiency in Python and deep learning frameworks (PyTorch/JAX), plus experience with GPU cluster job scheduling and scalable data pipelines.
• Proven publication record in top-tier venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML (or equivalent).
• Knowledge of sensor (RF, infrared, LiDAR, event camera) fundamentals; for radar, familiarity with FMCW, MIMO, Doppler signatures, radar point clouds/heatmaps, and raw ADC waveforms.
• Familiarity with MERL’s recent radar perception research, e.g., TempoRadar, SIRA, MMVR, RETR.

Mitsubishi Electric Research Laboratories (MERL) is seeking a self-motivated Ph.D. candidate in Computer Science, Electrical Engineering, or a related field for a 3 month internship focused on developing advanced machine learning algorithms for electric machine condition monitoring and predictive maintenance. The ideal candidate will have a strong background in machine learning and signal processing with a proven publication record, while experience in multi-modal data analysis or domain adaptation is preferred and knowledge of electric machines is a plus. The intern will collaborate with MERL researchers to design and develop novel machine learning algorithms, prepare technical reports, and contribute to manuscripts for top-tier scientific publications. This position requires onsite work at MERL, with a flexible start date.

Required Specific Experience

• Experience with Python and Matlab.