Internship Openings

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.

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.

MERL is seeking a highly motivated and qualified intern to conduct research on decentralized data assimilation for multi-physical and multi-component systems governed by large-scale nonlinear differential-algebraic equations (DAEs). The research will focus on the study, development, and efficient implementation of data assimilation algorithms for such complex systems. The ideal candidate will have a strong background in one or more of the following areas: nonlinear estimation and control, Bayesian methods, machine learning, graph theory, and optimization, with demonstrated expertise through peer-reviewed publications or equivalent experience. Proficiency in Julia or Python programming is required. Senior Ph.D. students in mechanical, electrical, chemical engineering, or related fields are encouraged to apply. The internship is typically 3 months in duration, with a flexible start date.

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

MERL seeks a motivated graduate student to develop scalable numerical solvers for systems composed of learned dynamical components. The internship will focus on the numerical challenges that arise when multiple neural-network-based component models are interconnected through physical constraints and feedback, forming large-scale coupled ODE/DAE systems. The intern will develop numerical methods to improve solver robustness and simulation speed for these large-scale systems. Candidates should have a strong foundation in numerical analysis and scientific computing, together with a solid background in machine learning and data-driven methods. Strong programming skills in Python, Julia, C/C++, or Matlab are expected. Senior PhD students in applied mathematics, scientific computing, computational engineering, or related fields are encouraged to apply. The internship is expected to last 3 months, with a flexible start date, and may be extended depending on research progress and mutual interest.

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

Join our cutting-edge research team to help advance the next generation of Embodied AI and Humanoid Robotics. As a research intern, you will develop AI technologies that enable humanoid robots to understand, reason, and interact with the physical world through complex manipulation, assembly, and tool-use tasks. This is a unique opportunity to contribute to impactful research with the goal of publishing at leading AI and robotics conferences.

What You'll Work On

Depending on your background and interests, projects may include:

• Embodied AI for dexterous manipulation, assembly, and tool use
• Vision-Language-Action (VLA) models and Foundation Models for robotic control
• World-Action Models (WAM) for long-horizon planning and decision making
• Learning from human demonstrations, teleoperation, and autonomous data collection
• Sim-to-real transfer, reinforcement learning, and real-world robot deployment

What We're Looking For

We are seeking highly motivated graduate students with:

• Strong research experience in robotics, embodied AI, machine learning, computer vision, or related fields
• Experience with deep learning frameworks such as PyTorch or JAX, and strong Python programming skills
• Familiarity with one or more of the following:
  • Vision-Language-Action (VLA) models
  • Foundation Models or multimodal AI
  • Reinforcement learning or imitation learning
  • Robot manipulation, motion planning, or control
  • Agentic AI systems for robotics

Preferred qualifications:

• Hands-on experience with humanoid or loco manipulators (e.g., Unitree G1)
• Experience with teleoperation systems (e.g., Pico, Sonic)
• Experience with robotics simulators (e.g., Isaac Sim, MuJoCo, Genesis)
• Familiarity with ROS/ROS 2 and real-world robot experimentation
• Familiarity with policy deployment on edge AI devices (e.g., Jetson GPUs)

Internship Details

• Duration: Approximately 4 months
• Start Date: Flexible
• Location: Cambridge, MA
• Objective: Conduct high-impact research leading to publications at premier AI and robotics conferences (e.g., CoRL, RSS, ICRA, IROS, NeurIPS, ICML)

If you are excited about building AI that enables robots to perform complex real-world tasks—including assembly, tool use, and dexterous manipulation—we encourage you to apply.

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

MERL is seeking a highly motivated Ph.D. intern to conduct research in robotic disassembly. The project will focus on developing learning-based methods for dexterous manipulation, robot perception, and sim-to-real deployment, including tasks such as small-object manipulation and handover. The selected intern will collaborate closely with MERL researchers to develop and implement novel algorithms, conduct experiments on robotic platforms, and publish research findings at top-tier robotics and AI conferences. The internship start date and duration are flexible.

Required Specific Experience

• Current enrollment in a Ph.D. program
• Research experience in robot learning, manipulation and perception
• Experience with robot simulation and sim-to-real transfer using platforms such as Isaac Lab, MuJoCo or Genesis
• Strong programming skills in Python or C++
• Demonstrated research potential and publications in leading robotics, computer vision, or machine learning venues

Interested applicants should apply with an updated CV and a list of relevant publications.

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

MERL is seeking a highly motivated and qualified intern to conduct research on fast electromagnetic transient (EMT) simulation of data-center power systems. The ideal candidate will have a strong background in one or more of the following areas: data-center power systems, power-system dynamics, AI load dynamics, EMT simulation, and power electronic converters and controls. Proficiency in Python, MATLAB, or C++ programming is required. Ph.D. students in electrical engineering or related fields are encouraged to apply. The internship is typically 3–6 months in duration, with a flexible start date.

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

MERL is seeking a highly motivated Ph.D. student to conduct research on Vision-Language-Action (VLA) models for robotic manipulation. Recent VLA models have demonstrated impressive capabilities in general-purpose robotic manipulation. This internship will investigate new methods for improving the reliability and effectiveness of VLA policies in real-world manipulation tasks. Potential research directions include exploring VLA-based approaches, multi-arm coordination, and adaptive behaviors for industrial manipulation tasks, with particular emphasis on reliability assessment, policy intervention, and corrective-action strategies.

The selected intern will work closely with MERL researchers to develop and implement novel algorithms, deploy and evaluate learning-based policies on robotic platforms, and conduct both simulation and real-world experiments. The intern will also disseminate research findings at leading robotics conferences, including ICRA, RSS, CoRL, and IROS. The internship start date and duration are flexible. Interested applicants are encouraged to apply with an updated CV and a list of relevant publications.

Required Specific Experience

• Current enrollment in a Ph.D. program
• Strong background in robot learning for manipulation
• Hands-on experience with Vision-Language-Action (VLA) models for robotics, such as pi0, OpenVLA, Octo, GR00T N1, or related state-of-the-art models
• Proficiency with ROS, Python, and deep learning frameworks such as PyTorch
• Experience with robotic simulation environments and sim-to-real transfer
• Experience deploying and evaluating learning-based policies on physical robotic platforms
• A strong publication record or demonstrated research potential

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

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 anomaly localization in industrial inspection setting using computer vision. The relevant topics in the scope include (but not limited to): few/zero/any-shot anomaly detection, generalist/continual/online/long-tailed/multi-view/noisy anomaly detection, how to incorporate large foundation models in image anomaly localization, etc. The candidates with experiences of image anomaly localization in industrial inspection settings (e.g., MVTec-AD, VisA, Real-IAD, or MANTA datasets) and usage of large foundation models are strongly preferred. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning, and the candidate is expected to have published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, AAAI, etc. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is required to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.

Required Specific Experience

• Experience with Python, PyTorch, and large foundation models (e.g. CLIP, Gemma, Qwen, etc.).

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

MERL is looking for a self-motivated intern to work on 3D object detection and recognition using computer vision. The relevant topics in the scope include (but not limited to): monocular/multi-view/aerial 3D object detection, 3D object detection with large foundation models, open-vocabulary or auto-vocabulary 3D object detection and recognition, domain adaptation or generalization in 3D object detection, data-efficient methods for 3D object detection, small 3D object detection, etc. The candidates with experiences of 3D object detection are strongly preferred. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning, and the candidate is expected to have published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, AAAI, etc. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is required to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.

Required Specific Experience

• Experience with Python, PyTorch, and 3D object detection datasets (e.g. ScanNet, WildDet3D-Data, etc.).

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

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.).

We are seeking graduate students interested in helping advance the fields of generative audio, source separation, speech enhancement, and robust ASR in challenging multi-source and far-field scenarios. The interns will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern's doctoral work.

The ideal candidates are senior Ph.D. students with experience in some of the following: audio signal processing, microphone array processing, probabilistic modeling, and deep generative modeling.

Multiple positions are available with flexible start dates (not just Spring/Summer but throughout 2027) and duration (typically 3-6 months).

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

We are seeking graduate students interested in helping advance the fields of sound event detection/localization, anomaly detection, and physics informed deep learning for machine sounds. The interns will collaborate with MERL researchers to derive and implement novel algorithms, record data, conduct experiments, integrate audio signals with other sensors (electrical, vision, vibration, etc.), and prepare results for publication.

The ideal candidates are senior Ph.D. students with experience in some of the following: audio signal processing, microphone array processing, physics informed machine learning, outlier detection, and unsupervised learning.

Flexible start dates and internship durations (typically 3–6 months) are available.

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

MERL is seeking a motivated and qualified intern to conduct research on condition monitoring and fault diagnosis. The intern will contribute to the development of advanced monitoring and diagnostic technologies, with applications that may include electric motors and motor-driven systems. Ideal candidates should be Ph.D. students with a solid background and publication record in one or more of the following research areas: fault diagnosis, prognosis, and health management; electric machine modeling and data analysis; machine learning techniques including transfer learning and domain adaptation for fault diagnosis. Strong programming skills in Python and familiarity with frameworks such as PyTorch are required. Experience with modeling and analysis of electric machines is highly desirable. Senior Ph.D. students in related fields (e.g., Electrical Engineering, Mechanical Engineering, Applied Physics) are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.

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

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 to fuse multi-modal time sequence data 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. Experience in time-sequence analysis, multimodal sensor fusion, or physics-informed machine learning is preferred. Knowledge of electric machines is a plus. The intern will collaborate with MERL researchers to design and develop novel 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 multi-modal sensor fusion.

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