Internship Openings

42 / 70 Intern positions were found.

Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

MERL expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of MERL's employees to perform their job duties may result in discipline up to and including discharge.

Qualified applicants for MERL internships are individuals who have or can obtain full authorization to work in the U.S. and do not require export licenses to receive information about the projects they will be exposed to at MERL. The U.S. government prohibits the release of information without an export license to citizens of several countries, including, without limitation, Cuba, Iran, North Korea and Syria (Country Groups E:1 and E:2 of Part 740, Supplement 1, of the U.S. Export Administration Regulations).

Rising to the challenges of COVID-19

MERL believes that having an internship be located in MERL's office allows for particularly good interaction between you and those that you will be working with at MERL. In addition, some intern projects, e.g., ones that require specialized laboratory equipment, can only be pursued in our office. Going forward, we expect that all internships will be in-person at MERL. If health and safety concerns do not permit this, we will reevaluate our plans and some internships might have to become remote.

It is a requirement at MERL that everyone working in MERL's space must be fully vaccinated. In order for you to have your internship at MERL, you will have to prove that you are fully vaccinated when you arrive at MERL, i.e., by showing your vaccination card.


  • DA1899: Physics-informed scientific machine learning

    • The Data Analytics Group at MERL is seeking a highly motivated, qualified individual to join our internship program in the summer of 2023. The ideal candidate will be a Ph.D. student specializing in engineering, applied mathematics, computer science or similar fields with solid background in scientific machine learning, deep learning, and non-convex optimization. Research exposure to one of the following is very desirable but not necessary: PDE-constrained optimization, Koopman theory, dynamical systems, operator learning (DeepONet, FNO, etc.), and Physics-informed Neural Nets (PINNs). Ideal candidate is familiar with PyTorch, TensorFlow, or Jax. Publication of results obtained during the internship is expected. The starting date is flexible and the internship will last about 12 weeks.

    • Research Areas: Computational Sensing, Dynamical Systems, Machine Learning
    • Host: Saleh Nabi
    • Apply Now
  • DA1960: Unconventional robotic manipulation

    • This internship will be in the domain of robotic manipulation beyond the conventional methods such as 2-jawed grippers without feedback, mounted on a 6-DoF robot arm. The ideal candidate would have a wide mechatronic background as well as some familiarity with machine learning and machine vision, strong programming skills in Python and C on Linux and embedded systems (especially Arduino), CAD skills in FreeCAD, OpenSCAD, and KiCAD for mechatronic design, and hands-on hardware prototyping skills (i.e. CNC, 3D printing, soldering). A wild imagination is a definite plus.

    • Research Areas: Data Analytics, Machine Learning, Robotics
    • Host: Bill Yerazunis
    • Apply Now
  • DA1889: Safe and robust reinforcement learning

    • MERL is seeking a motivated and qualified individual to conduct research in safe robust reinforcement learning (RL). The ideal candidate should have solid background in RL, e.g. CMDP, and RMDP theories. Knowledge of dynamical system theory and nonlinear control theory is a plus, but not a requirement. Submission of the results produced during the internship is anticipated, i.e., ICML, ICLR, NeurIPS. Duration of the internship is expected to be 3 months. Start date is flexible

    • Research Areas: Machine Learning
    • Host: Mouhacine Benosman
    • Apply Now
  • DA1926: Robotic Manipulation Control using VisuoTactile Sensing

    • MERL is looking for a highly motivated individual to work on robust, closed-loop control of robotic manipulation system using vision and tactile feedback. The research will develop novel optimization and control techniques that can be used for closed-loop control of manipulation systems. The ideal candidate should have experience in either one or multiple of the following topics: optimization for contact-rich systems, stochastic optimization of non-linear systems, stochastic model-predictive control and reinforcement learning. Senior PhD students in robotics and engineering with a focus on contact-rich manipulation are encouraged to apply. Prior experience working with physical robotic systems (and vision & tactile sensors) is required as results need to be implemented on physical hardware. A successful internship will result in submission of results to peer-reviewed conferences and journals. Good coding skills in Python and state-of-the-art optimization packages like IPOPT, SNOPT, etc. is required. The expected duration of internship is 3-4 months with start date in May/June 2023. This internship is preferred to be onsite at MERL.

    • Research Areas: Artificial Intelligence, Control, Machine Learning, Optimization, Robotics
    • Host: Devesh Jha
    • Apply Now
  • DA1900: Data-driven estimation and control for large-scale dynamical systems

    • The Data Analytics Group at MERL is seeking a highly motivated, qualified individual to join our internship program in the summer of 2023. The ideal candidate will be a Ph.D. student specializing in engineering, applied mathematics, computer science or similar fields with solid background in control, estimation, and dynamical systems. Research exposure to one of the following is very desirable but not necessary: reduced-order models (ROMs), reinforcement learning, nonlinear control, PDEs, and robust control. Publication of results obtained during the internship is expected. The starting date is flexible and the internship will last about 12 weeks.

    • Research Areas: Control, Dynamical Systems, Machine Learning
    • Host: Saleh Nabi
    • Apply Now
  • DA1935: Robot Learning Algorithms

    • MERL is looking for a highly motivated and qualified PhD student in the areas of machine learning and robotics, to participate in research on advanced algorithms for learning control of robots and other mechanisms. Solid background and hands-on experience with various machine learning algorithms is expected, and in particular with deep learning algorithms for image processing and object detection. Exposure to deep reinforcement learning and/or learning from demonstration is highly desirable. Familiarity with the use of machine learning algorithms for system identification of mechanical systems would be a plus, along with background in other areas of automatic control. Solid experimental skills and hands-on experience in coding in Python and PyTorch are required for the position. Some familiarity with classical mechanics and computational physics engines would be helpful, but is not required. The position will provide opportunities for exploring fundamental problems in incremental learning in humans and machines, leading to publishable results. The starting date of the internship is flexible, and applications outside of the peak summer season are encouraged, too.

    • Research Areas: Artificial Intelligence, Computer Vision, Control, Machine Learning, Robotics
    • Host: Daniel Nikovski
    • Apply Now
  • DA1895: Human Robot Interaction

    • MERL is looking for a self-motivated and qualified candidate to work on human-robot-interaction projects. The ideal candidate is a PhD student and should have experience and records in one or multiple of the following areas. 1) Control, estimation and perception for Robotic manipulation 2) Experience in shared autonomy between robot and humans and intent recognition 3) Learning from demonstration algorithms applied to robotic manipulators 4) Machine learning techniques for modeling and control as well as regression and classification problems. 5) Experience in working with robotic systems and familiarity with one physics engine simulator like Mujoco, pyBullet, pyDrake. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms to solve complex manipulation tasks that involve human robot collaboration. Exceptional programming skills are required, including Python and ROS. The expectation is that the research will lead to one or more scientific publications. Typical internship length is 3-4 months.

    • Research Areas: Artificial Intelligence, Control, Machine Learning, Robotics
    • Host: Diego Romeres
    • Apply Now
  • CA1929: Driver Classification for Future Mobility

    • MERL is looking for an intern to conduct research on driver classification/characterization for electrified, connected, or sustainable mobility. The intern will develop modeling techniques for optimizing/personalizing the operation of electric vehicles using driver data and/or crowdsourced data. The ideal candidate will have experience in either one or multiple of the following topics: vehicle control, energy management of electric vehicle, statistical estimation, connected vehicles, machine learning, numerical optimization, and reinforcement learning. Good programming skills in MATLAB or Python are required. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, etc.) is a plus. Graduate students in engineering, mathematics, or similar quantitative disciplines are encouraged to apply. Publication of relevant results in conference proceedings or journals is encouraged and expected. The expected duration of the internship is 3-6 months. The start date is flexible.

    • Research Areas: Control, Machine Learning, Optimization
    • Host: Marcel Menner
    • Apply Now
  • CA1954: Control and Motion Planning for Quadrotors

    • MERL is seeking a highly motivated and qualified intern to work on fundamental algorithms for motion planning and control of multiple autonomous quadrotor aerial vehicles. The ideal candidate should have a background in nonlinear control, estimation theory, and applied optimization. The candidate should have experience in one or multiple of the following topics: optimal control, Lyapunov stability theory, quadrotor dynamics, Kalman filtering, particle filtering, and machine learning. Capability of implementing the designs and algorithms in Matlab and Simulink is expected, and experience with platforms such as the Crazyflie is a plus. Publication of relevant results in conference proceedings or journals is expected. MS or PhD students in control, robotics, electrical engineering, computer science, or related areas, are encouraged to apply. The expected duration of the internship is 3-6 months and the start date is flexible.

    • Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Marcus Greiff
    • Apply Now
  • CA1905: Coordination and Control of Connected Autonomous Vehicles

    • MERL is looking for a highly motivated individual to work on optimization-based techniques for coordination and control of connected autonomous vehicles (CAVs), in the presence of other CAVs and human driven vehicles (HDVs). The research will involve the development, implementation, and validation of optimization-based coordinated control of vehicles through traffic intersections and/or merging scenarios. The ideal candidate should have experience in either one or multiple of the following topics: vehicle modeling and/or traffic modeling, mixed-integer programming, (stochastic) model predictive control, reinforcement learning, data-driven (e.g., Gaussian Process) modeling, hybrid dynamical systems, coordination and control of CAVs. Knowledge of one or multiple vehicle and/or traffic simulators (SUMO, CARLA, CarSim, Vissim, etc.) is a plus. Publication of relevant results in conference proceedings or journals is expected. Good programming skills in Matlab are required and knowledge in Python or C/C++ is a merit. PhD students in engineering, mathematics, or similar are encouraged to apply. The expected duration of the internship is 3-6 months and the start date is flexible.

    • Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Rien Quirynen
    • Apply Now
  • CA1904: Numerical Optimal Control for Hybrid Dynamical Systems

    • MERL is looking for a highly motivated individual to work on tailored computational algorithms for numerical optimal control of hybrid dynamical systems and applications for decision making, motion planning and control of autonomous systems. The research will involve the study and development of numerical optimal control methods for systems with continuous dynamics and discrete logic, nonsmooth and/or switched dynamics, and the implementation and validation of such algorithms for industrial applications, e.g., related to autonomous driving and robotics. The ideal candidate should have experience in either one or multiple of the following topics: mixed-integer programming (MIP), mathematical programs with complementarity constraints (MPCCs), modeling and formulation of optimal control problems for hybrid dynamical systems, convex and non-convex optimization, machine learning and real-time optimization. PhD students in engineering or mathematics, especially with a focus on MIPs, MPCCs or numerical optimal control, are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months and the start date is flexible.

    • Research Areas: Control, Machine Learning, Optimization, Robotics
    • Host: Rien Quirynen
    • Apply Now
  • CA1939: Motion Planning, Estimation and Control for Articulated Vehicles

    • MERL is seeking a highly motivated and qualified intern to collaborate with multiple researchers on the improvement, real-time implementation and experimental validation of algorithms for path/motion planning, constrained state estimation, optimal control and reference tracking in autonomous articulated vehicles. The ideal candidate should have a background in either path/motion planning, state and parameter estimation and/or model predictive control (MPC) for autonomous (articulated) vehicles, and the candidate should have experience in one or multiple of the following topics: optimal control, MPC, vehicle dynamics, A* search, RRT, Kalman filtering, particle filtering, and machine learning. Capability of implementing the designs and algorithms in Matlab and Simulink, and using C/C++ code generation is expected. Any experience with dSPACE (e.g., MicroAutoBox or Scalexio), CasADi, and/or experience with vehicle experiments or simulators (e.g., TruckSim or CarSim) is a plus. Publication of relevant results in conference proceedings or journals is expected. MS or PhD students in control, robotics, electrical and mechanical, or related areas, are encouraged to apply. The expected duration of the internship is 3-6 months and the start date is flexible.

    • Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Rien Quirynen
    • Apply Now
  • CA1928: Multi-agent systems for resource monitoring

    • MERL is looking for a highly motivated individual to develop planning and control algorithms for multi-agent systems for resource monitoring. The ideal candidate has experience in multi-agent motion planning and data-driven sequential decision-making. The ideal candidate will have published in one or more of these topics: planning over discrete spaces, task scheduling and assignment, vehicle routing and scheduling problems, multi-arm bandits, reinforcement learning, and planning and control of aerial and ground robots. The candidate should have a working knowledge of ROS and Python/C++ since the internship will include validation in various simulation/hardware testbeds at MERL. The minimum duration of the internship is 3 months; the start time is Summer/Fall 2023.

    • Research Areas: Artificial Intelligence, Control, Machine Learning, Optimization, Robotics
    • Host: Abraham Vinod
    • Apply Now
  • MD1896: Machine Learning based Electric Machine Design

    • MERL is seeking a motivated and qualified intern to conduct research on machine learning based electric machine design and optimization. Ideal candidates should be Ph.D. students with solid background and publication record in electric machine design and optimization, as well as machine learning especially for inverse design. Hands-on experiences with the implementation of optimization algorithms, machine learning and deep learning methods are required. Strong programming skills using Python/PyTorch are expected. Knowledge and experience with electric machine principle, design and finite-element analysis are highly desirable. Start date for this internship is flexible and the duration is 3-6 months.

    • Research Areas: Applied Physics, Machine Learning, Optimization
    • Host: Bingnan Wang
    • Apply Now
  • MD1918: Robust/safe learning for motion planning and control

    • MERL is seeking a highly motivated and qualified individual to conduct research in the integration of model- and learning-based control to achieve high performance with guaranteed safety and robustness. The ideal candidate should have solid backgrounds in dynamical system estimation and uncertainty quantification, model-based control and adaptive/learning control for output tracking, and coding skills. Prior experience on ultra-high precision motion control system is a big plus. Ph.D. students in mechatronics and control are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

    • Research Areas: Control, Dynamical Systems, Machine Learning, Optimization
    • Host: Yebin Wang
    • Apply Now
  • MD1897: Electric Motor Fault Detection

    • MERL is seeing a motivated and qualified individual to conduct research on electric machine fault analysis and detection. Ideal candidates should be Ph.D. students with solid background and publication record in one more research area: electric machine design, analysis, fault detection, and predictive maintenance. Research experiences on modeling and analysis of electric machines and fault detection are required. Hands-on experience with permanent magnet synchronous motor (PMSM) design, data analysis, and machine learning techniques are highly desirable. Start date for this internship is flexible and the duration is 3-6 months.

    • Research Areas: Applied Physics, Machine Learning, Signal Processing
    • Host: Bingnan Wang
    • Apply Now
  • MD1891: Electric machine monitoring technologies

    • MERL is looking for a self-motivated intern to work on electric machine monitoring, fault detection, and predictive maintenance. The ideal candidate would be a Ph.D. candidate in electrical engineering or computer science with solid research background in electric machines, signal processing, and machine learning. Proficiency in MATLAB and Simulink is necessary. The intern is expected to collaborate with MERL researchers to perform simulations, analyze experimental data, and prepare manuscripts for scientific publications. The total duration is anticipated to be 3 months and the start date is flexible. This internship requires work that can only be done at MERL.

    • Research Areas: Electric Systems, Machine Learning, Signal Processing
    • Host: Dehong Liu
    • Apply Now
  • MD1917: Learning and Optimization for Motor Drives

    • The Electric Machines and Devices team at MERL is seeking motivated and qualified individuals to assist in the development of advanced motor drives technologies. The project goals are twofold: (i) explore model-based optimization methods to design switching voltage sequence for switching losses minimization; (ii) machine learning methods to model the nonlinear flux to current relationship (flux-map) of synchronous machines with spatial harmonics from the real-time experimental data during the commissioning process. The background of the ideal candidate is expected to overlap with at least one of the project goals, if not two. Senior PhD candidates working in mixed integer optimization, model predictive control or machine learning are encouraged to apply. Capability of implementing the algorithm in MATLAB and coding in C are a necessity. Knowledge of motor drives and hands-on experience in real-time systems are a plus. The expected duration of the internship is 3-6 months, preferably onsite at MERL and the start date is flexible.

    • Research Areas: Control, Electric Systems, Machine Learning, Optimization
    • Host: Anantaram Varatharajan
    • Apply Now
  • ST1750: THz (Terahertz) Sensing

    • The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in THz (Terahertz) sensing. Expertise in statistical inference, unsupervised anomaly detection, and deep learning (spatial-temporal representation learning) is required. Previous hands-on experience in THz data analysis is a plus. Familiarity with python and deep learning libraries is a must. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with collaborators, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date.

    • Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Optimization, Signal Processing
    • Host: Perry Wang
    • Apply Now
  • CI1953: Robust AI for Cybersecurity Systems

    • MERL is seeking a highly motivated and qualified intern to work on robust AI for cybersecurity systems. The intern will be expected to conduct research on enhancing the resilience of cybersecurity systems against advanced attacks. The ideal candidate would have significant research experience in the following topics: robust machine learning and anomaly detection, defenses against adversarial examples, and cybersecurity event data processing. A mature understanding of modern machine learning methods, proficiency with Python, and familiarity with deep learning frameworks are expected. Proficiency with other programming languages and software development experience is a plus. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply. This position will be available for Summer/Fall of 2023, with an expected duration of 3 months and flexible start dates.

    • Research Areas: Artificial Intelligence, Data Analytics, Machine Learning
    • Host: Ye Wang
    • Apply Now
  • CI1948: Human-Machine Interface with Biosignal Processing

    • MERL is seeking an intern to work on research for human-machine interface with multi-modal bio-sensors. The ideal candidate is an experienced PhD student or post-graduate researcher having an excellent background in brain-machine interface (BMI), deep learning, mixed reality (XR), remote robot manipulation, and bio sensing. The expected duration of the internship is 3-6 months, with a flexible start date.

    • Research Areas: Artificial Intelligence, Machine Learning, Robotics
    • Host: Toshi Koike-Akino
    • Apply Now
  • CI1949: Machine Learning for Electric Design Automation

    • MERL is seeking a highly motivated and qualified intern for electric design automation (EDA). The ideal candidate will be expected to carry out research on machine learning for EDA and high-level synthesis (HLS) to improve hardware efficiency of various digital signal processing algorithms and artificial intelligence (AI) systems. The candidate is expected to have solid knowledge of deep learning, reinforcement learning, symbolic learning, decision making, graph neural networks, and hands-on experience of HLS, FPGA prototyping, and hardware description language (HDL).

    • Research Areas: Artificial Intelligence, Machine Learning, Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • CI1950: Quantum Machine Learning

    • MERL is seeking an intern to work on research for quantum machine learning (QML). The ideal candidate is an experienced PhD student or post-graduate researcher having an excellent background in quantum computing, deep learning, and signal processing. Proficient programming skills with PyTorch and PennyLane will be additional assets to this position.

    • Research Areas: Artificial Intelligence, Machine Learning, Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • CI1946: Robust, Private, and Efficient Machine Learning

    • MERL is seeking highly motivated and qualified interns to work on fundamental machine learning techniques for robustness, privacy, and efficiency. The ideal candidates would have significant research experience in one or more of the following topics: robust machine learning methods, defenses against adversarial examples, privacy issues in machine learning, membership inference attacks, federated/distributed learning, and/or efficient/Green AI. A mature understanding of modern machine learning methods, proficiency with Python, and familiarity with deep learning frameworks are expected. Proficiency with other programming languages and software development experience is a plus. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply. Multiple positions are available throughout 2023 (Spring/Summer of course, but also as early as January), with expected durations of 3-6 months and flexible start dates.

    • Research Areas: Artificial Intelligence, Machine Learning, Signal Processing
    • Host: Ye Wang
    • Apply Now
  • CV1938: Component transfer learning for RL and robotic applications

    • MERL is offering a new research internship opportunity in the field of Transfer Learning for Deep RL. The position requires a strong background in Deep RL, excellent programming skills and experience with robotics is preferred. The position is open to graduate students on a PhD track only, and the length of the internship is three months with the possibility of extending if required. The intern is expected to disseminate this research in top tier scientific conferences such as RSS, IROS, ICRA etc., and if applicable, help with filing associated patents. Start and end dates are flexible.

    • Research Areas: Artificial Intelligence, Machine Learning, Robotics
    • Host: Radu Corcodel
    • Apply Now
  • CV1920: Conditional Diffusion Models in Computer Vision

    • We seek a highly motivated intern to conduct original research in conditional diffusion models for computer vision tasks. We are interested in applications to various tasks including image editing, multimodal generation, and image-to-image translation. The successful candidate will collaborate with MERL researchers to design and implement new models, conduct experiments, and prepare results for publication. The candidate should be a PhD student (or postdoc) in computer vision and machine learning with a strong publication record. Strong programming skills, experience developing and implementing new models in deep learning platforms such as PyTorch, and broad knowledge of machine learning and deep learning methods are expected. Previous experience in diffusion models is required.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Tim Marks
    • Apply Now
  • CV1902: Human-Robot Interaction for Robotic Manipulation

    • MERL is looking for a highly motivated and qualified intern to work on human-robot interaction (HRI) research. The ideal candidate would be a Ph.D. student with a strong background in HRI, focusing on robotic manipulation, deep learning, probabilistic modeling, or reinforcement learning. Several topics are available for consideration, including Intent Recognition, Shared Autonomy, Human-Robot Teaming for Automation /Manufacturing, Human-Robot Handover, Computer Vision for HRI, and Learning for Robot Programming. Experience working with robotics hardware and physics engine simulators like PyBullet, Issac Gym, Mujoco, or Gazebo is preferred. Proficiency in Python programming is necessary, and experience with ROS is a plus. The successful candidate will collaborate with MERL researchers, and publication of the relevant results is expected. The start date is flexible, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and list of publications in related topics.

    • Research Areas: Computer Vision, Machine Learning, Robotics
    • Host: Siddarth Jain
    • Apply Now
  • CV1925: Symmetries, equivariance and invariance in deep learning

    • MERL is seeking an intern to conduct research in the areas of learning symmetries from data and equivariant neural networks for applications in computer vision. The ideal candidate is a PhD student with experience in deep learning and computer vision and a good publication record at top-tier venues. Prior knowledge and experience with group theory/geometry and equivariant neural networks are a big plus. Good Python and Pytorch/Tensorflow skills are required. Publication of results in conference proceedings and journals is expected. The expected duration of the internship is 3 months and the start date is flexible.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Suhas Lohit
    • Apply Now
  • CV1913: Roadway Maintenance Using Computer Vision

    • MERL is looking for a self-motivated intern to work on roadway maintenance using computer vision. The relevant topics include (but not limited to): road defect localization and classification, detection of key objects in road scenes, and pavement surface evaluation. Candidates with experience in road defect detection and object detection in road scenes 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, or AAAI. 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.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Kuan-Chuan Peng
    • Apply Now
  • CV1930: Meta-Algorithmic Learning for Vision-based Robotic Manipulation

    • MERL is looking for a self-motivated intern to work on problems at the intersection of computer vision and robotic manipulation for solving tasks such as vision-based robotic tool manipulation. The ideal candidate would be a PhD student with strong mathematical background in machine learning/reinforcement learning, modeling contact-physics for object manipulation, and experience in working with and training deep models on large scale computer vision datasets. Proficiency in PyTorch and (differentiable) robotic simulators is expected. Knowledge of meta-learning, hierarchical RL, self-supervised learning, and scene graph based visual reasoning would be useful. The intern will conduct original research with MERL researchers towards scientific publications.

    • Research Areas: Artificial Intelligence, Computer Vision, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Anoop Cherian
    • Apply Now
  • CV1912: Multimodal Embodied AI

    • MERL is looking for a self-motivated intern to work on problems at the intersection of visual understanding, audio processing, language models, and embodied navigation AI (see our recent NeurIPS 2022 paper for the context). The ideal candidate would be a senior 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 developing deep learning methods for audio-visual-language data. Expertise in popular embodied AI simulation environments as well as a strong background in reinforcement learning will be beneficial. The intern is expected to collaborate with researchers in computer vision and speech teams at MERL to develop algorithms and prepare manuscripts for scientific publications.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Robotics, Signal Processing
    • Host: Anoop Cherian
    • Apply Now
  • CV1907: Novel View Synthesis of Dynamic Scenes

    • MERL is looking for a highly motivated intern to work on an original research project in rendering dynamic scenes from novel views. A strong background in 3D computer vision and/or computer graphics is required. Experience in the latest advances of deep learning, such as neural radiance fields, is an added plus and will be valued. The successful candidate is expected to have published at least one paper in a top-tier computer vision/graphics or machine learning venue, such as CVPR, ECCV, ICCV, SIGGRAPH, 3DV, ICML, ICLR, NeurIPS or AAAI, and possess solid programming skills in Python and popular deep learning frameworks like Pytorch. The goal would be for such a candidate to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The position is available for graduate students on a Ph.D. track. Duration and start dates are flexible but are expected to last for at least 3 months. This internship is preferred to be onsite at MERL’s office in Cambridge, MA.

    • Research Areas: Computer Vision, Machine Learning
    • Host: Moitreya Chatterjee
    • Apply Now
  • CV1922: Vital Signs from video using computer vision and machine learning

    • MERL is seeking a highly motivated intern to conduct original research in estimating vital signs such as heart rate, heart rate variability, and blood pressure from video of a person. The successful candidate will use the latest methods in deep learning, computer vision, and signal processing to derive and implement new models, collect data, conduct experiments, and prepare results for publication, all in collaboration with MERL researchers. The candidate should be a PhD student in computer vision with a strong publication record and experience in computer vision, signal processing, machine learning, and health monitoring. Strong programming skills (Python, Pytorch, Matlab, etc.) are required.

    • Research Areas: Computer Vision, Machine Learning, Signal Processing
    • Host: Tim Marks
    • Apply Now
  • CV1901: Deep Learning for Robotic Grasping

    • MERL is looking for a highly motivated and qualified intern to work on computer vision for visual feedback and robotic grasping. The ideal candidate would be a Ph.D. student with a strong background in deep learning and robotic manipulation. Several topics are available for consideration, including state estimation of objects at high speed, closed-loop reactive grasping, vision-guided dynamic object grasping, goal-driven grasping, deformable object grasping, and grasping in clutter. The project requires the development of novel algorithms with implementation and evaluation on a robotic platform. Experience working with a physics engine simulator like PyBullet, Issac Gym, Mujoco, or Gazebo is preferred. Proficiency in Python programming is necessary, and experience with ROS is a plus. The successful candidate will collaborate with MERL researchers, and publication of the relevant results is expected. The start date is flexible, and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and list of publications in related topics.

    • Research Areas: Computer Vision, Machine Learning, Robotics
    • Host: Siddarth Jain
    • Apply Now
  • CV1909: Open-World Object Detection

    • MERL is looking for a highly motivated intern to work on an original research project in open-world object detection. A strong background in computer vision and deep learning is required. Experience in the latest advances in object detection, incremental learning, and open-world object detection is an added plus and will be valued. The successful candidate is expected to have published at least one paper in a top-tier computer vision or machine learning venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS or AAAI, and possess solid programming skills in Python and popular deep learning frameworks like Pytorch. The position is available for graduate students on a Ph.D. track. Duration and start dates are flexible but are expected to last for at least 3 months. This internship is preferred to be onsite at MERL’s office in Cambridge, MA.

    • Research Areas: Computer Vision, Machine Learning
    • Host: Mike Jones
    • Apply Now
  • CV1927: Deep Multimodal Learning for 3D Vision

    • MERL is seeking an intern to conduct research in the area of multimodal 3D vision using modalities such as RGB images and LIDAR. The focus will be on building novel learning algorithms for core applications like robust object detection and semantic segmentation. A good candidate is a PhD student with experience in deep learning and computer vision with a publication record. Prior knowledge and experience in one or more of the above areas are strongly preferred. Good Python and Pytorch/Tensorflow skills are required. Publication of results in conference proceedings and journals is expected. The expected duration of the internship is 3 months and the start date is flexible.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Suhas Lohit
    • Apply Now
  • CV1906: Next Generation Self-Supervised Learning

    • MERL is looking for a highly motivated intern to work on an original research project in self-supervised learning. A strong background in computer vision and deep learning is required. Experience in audio-visual (multimodal) learning is an added plus and will be valued. The successful candidate is expected to have published at least one paper in a top-tier computer vision or machine learning venue, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS or AAAI, and possess solid programming skills in Python and popular deep learning frameworks like Pytorch. The goal would be for such a candidate to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The position is available for graduate students on a Ph.D. track. Duration and start dates are flexible but are expected to last for at least 3 months. This internship is preferred to be onsite at MERL’s office in Cambridge, MA.

    • Research Areas: Computer Vision, Machine Learning, Speech & Audio
    • Host: Moitreya Chatterjee
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  • SA1959: Metasurfaces for machine vision

    • We seek highly qualified candidates for research on co-design and optimization of metasurfaces and machine vision algorithms, with a particular interest in polarization. Strong candidates will have a background in metasurface optics, fluency with FDTD and RCWA simulation tools, and some familiarity with optimization methods used in computer vision and machine learning.

    • Research Areas: Applied Physics, Computational Sensing, Machine Learning
    • Host: Matt Brand
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  • SA1874: Audio source separation and sound event detection

    • We are seeking graduate students interested in helping advance the fields of source separation, speech enhancement, robust ASR, and sound event detection/localization 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. The ideal candidates are senior Ph.D. students with experience in some of the following: audio signal processing, microphone array processing, probabilistic modeling, sequence to sequence models, and deep learning techniques, in particular those involving minimal supervision (e.g., unsupervised, weakly-supervised, self-supervised, or few-shot learning). Multiple positions are available throughout 2023 (Spring/Summer of course, but also as early as January), with expected durations of 3-6 months and flexible start dates.

    • Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    • Host: Gordon Wichern
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  • MS1903: Bayesian Optimization and MPC for Net-Zero Energy Buildings

    • MERL is looking for a highly motivated and qualified candidate to work on Bayesian Optimization and predictive control for net-zero energy buildings. The ideal candidate will have a strong understanding of control, optimization, and/or machine learning with expertise demonstrated via, e.g., publications, in at least one of: Bayesian optimization, (stochastic) model predictive control, reinforcement learning, controller tuning; additional understanding of energy systems is a plus. Hands-on programming experience with numerical optimization solvers and Python is preferred. PhD students are strongly preferred, as an expected outcome of the internship is a publication in a high-tier venue. The minimum duration of the internship is 12 weeks; start time is flexible.

    • Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization
    • Host: Ankush Chakrabarty
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  • MS1957: Estimation and Model Structure Identification for Digital Twins

    • MERL is looking for a highly motivated and qualified candidate to work on estimation and model structure identification for digital twins of multi-physical systems. The research will involve study and development of white-box and grey-box model calibration and identification methods suitable for large-scale systems. The ideal candidate will have a strong background in one or multiple of the following topics: nonlinear estimation, model identification, optimization, data-driven and reduced order modeling, and machine learning; with expertise demonstrated via, e.g., peer-reviewed publications. Prior programming experience in Julia/Modelica is a plus. Senior PhD students in mechanical, electrical, chemical engineering or related fields are encouraged to apply. The typical duration of internship is 3 months and start date is flexible.

    • Research Areas: Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization
    • Host: Vedang Deshpande
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  • MS1851: Dynamic Modeling and Control for Grid-Interactive Buildings

    • MERL is looking for a highly motivated and qualified candidate to work on modeling for smart sustainable buildings. The ideal candidate will have a strong understanding of modeling renewable energy sources, grid-interactive buildings, occupant behavior, and dynamical systems with expertise demonstrated via, e.g., peer-reviewed publications. Hands-on programming experience with Modelica is preferred. The minimum duration of the internship is 12 weeks; start time is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • Research Areas: Machine Learning, Multi-Physical Modeling, Optimization
    • Host: Chris Laughman
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