Internship Openings

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., radar, 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 May 2027.

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 and diffusion-based frameworks
• Datasets & evaluation: Hands-on experience with open large-scale multi-sensor datasets (e.g., OctoNet, CUHK-X) and open radar datasets (e.g., MMVR, HuPR, HIBER, RT-Pose, K-Radar). Ability to design reasoning-centric benchmarks.
• 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, RAPTR, REXO.

The pay range for this internship position will be 6-8K per month.

The Computational Sensing team at Mitsubishi Electric Research Laboratories (MERL) is seeking a highly motivated PhD student for a research internship focused on generative modeling, probabilistic inference, and optimization. Potential research directions include diffusion and flow-based generative models, efficient and controllable generation, Bayesian inference and uncertainty quantification, and generative methods for optimization. Applications may include materials and molecular discovery, inverse design, imaging, and modeling of complex physical and dynamical systems. Specific research topics will be developed collaboratively based on mutual interests and expertise. We particularly value creativity, strong mathematical and computational skills, independent thinking, and complementary expertise across disciplines. Publication of research results is expected. The internship is anticipated to last at least 3 months, with a flexible start date.

Specific Requirements:

• PhD student in computer science, applied mathematics, statistics, computational chemistry, materials science, engineering, or a related field.

• Strong research background in one or more of the following areas: generative modeling, probabilistic machine learning, stochastic processes, optimization, or computational modeling of physical and molecular systems.

• Proficiency in Python and experience with modern machine learning frameworks or scientific computing tools.

The pay range for this internship position will be 6-8K per month.

The Computational Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research on multi-modal sensing and understanding —algorithms that can understand, explain, and act on multi-sensor data (e.g., RF, infrared, LiDAR, event camera). Ideal candidates will be comfortable bridging state-of-the-art perception (detection/segmentation/tracking) with higher-level semantic understanding and reasoning capabilities. Experience with text, visual, and multimodal reasoning is a plus. 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.

Required Specific Experience

• Expertise in physical sensing across RF (radar, UWB, Wi-Fi), infrared, LiDAR, and event-camera modalities. Experienced with radar systems and concepts including FMCW and MIMO configurations, Doppler signature interpretation, radar point cloud and heatmap representations, and raw ADC waveforms;
• Solid understanding of state-of-the-art transformer-based (e.g., DETR) and diffusion-based (e.g., DiffusionDet) frameworks;
• Demonstrated work in text-, visual-, and multimodal semantic understanding and reasoning.
• 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).
• 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).

The pay range for this internship position will be 6-8K per month.

The Computational Sensing team at MERL is seeking a highly motivated Ph.D. student for a research internship in AI-aided fluid modeling and engineering design. The goal is to develop reasoning-driven and self-improving methods that use simulation feedback to discover and refine physical models, computational algorithms, and engineering designs. Potential directions include automated experimentation, iterative program improvement, and recursive self-improvement of modeling and optimization strategies. The ideal candidate will be a Ph.D. student in engineering, applied mathematics, computer science, or related fields with a solid background and publication record in one or more of the following areas: foundation models and agentic AI, automated algorithm discovery and refinement, scientific machine learning, computational fluid dynamics, or engineering optimization. Programming skills in Python and experience with machine learning frameworks or scientific computing tools are required. Publication of the results obtained during the internship is expected. The duration is anticipated to be at least 3 months with a flexible start date.

The pay range for this internship position will be $6-8K per month.