Internship Openings

74 Intern positions are currently open.

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. We expect that all internships during 2022 will be in-person at MERL.

It is of course possible that COVID will take a significant turn for the worse in 2022. If that happens, 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, ie by showing your vaccination card.


  • SA1689: Audio source separation and sound event detection

    • We are seeking a graduate student 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 intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. The ideal candidate would be a senior Ph.D. student 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). The internship will take place during spring/summer 2022 with an expected duration of 3-6 months and a flexible start date. 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, Speech & Audio
    • Host: Gordon Wichern
    • Apply Now
  • SA1686: Multimodal scene understanding

    • We are looking for a graduate student interested in helping advance the field of multi-modal scene understanding, with a focus on detailed captioning of a scene using natural language. The intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. The ideal candidate would be a senior Ph.D. student with experience in deep learning for audio-visual, signal, and natural language processing. The expected duration of the internship is 3-6 months, and start date 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: Artificial Intelligence, Speech & Audio
    • Host: Chiori Hori
    • Apply Now
  • MD1757: ML based Digital Pre-distortion (DPD) for PA

    • MERL is looking for a talented intern to work on the next generation Digital-predistortion algorithms for power amplifier linearization such as 5G. The development of a DPD system involves aspects of signal processing and statistical algorithm design, RF components and instrumentation, digital hardware and software. It is therefore both a challenging and intellectually rewarding experience. This will involve MATLAB coding, interfacing to test equipment such as power sources, signal generators and analyzers and construction and calibration of RF component assemblies. The ideal candidate should have knowledge and experience in adaptive signal processing, machine learning, and radio communication. Good practical laboratory skills are needed. RF semiconductor devices and circuit knowledge is a plus. Duration is 3 to 6 months. This internship requires work that can only be done at MERL.

    • Research Areas: Electronic and Photonic Devices, Machine Learning, Signal Processing
    • Host: Rui Ma
    • Apply Now
  • MD1761: Blind signal decomposition

    • MERL is seeking a self-motivated intern to work on blind signal decomposition. The ideal candidate would be a senior PhD student with solid background in signal processing, sparse representation, and optimization. Prior experience in array signal processing, compressive sensing, and spectrum analysis is preferred. Skills in Python and/or Matlab are required. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. The expected duration of the internship is 3 months and the start date is flexible. This internship requires work that can only be done at MERL.

    • Research Areas: Computational Sensing, Optimization, Signal Processing
    • Host: Dehong Liu
    • Apply Now
  • MD1745: Electric machine operation analysis

    • MERL is looking for a self-motivated intern to work on electric machine experiments and signal processing. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in electric machines, power electronics, and signal processing. Experience in dSPACE is required. Proficiency in MATLAB and simulink is necessary. The intern is expected to collaborate with MERL researchers to carry out experiments, analyze experimental data, and prepare manuscripts for scientific publications. The total duration is 3 months. This internship requires work that can only be done at MERL.

    • Research Areas: Data Analytics, Electric Systems, Signal Processing
    • Host: Dehong Liu
    • Apply Now
  • MD1771: Machine Learning for Electric Machine Design Optimization

    • MERL is seeking a motivated and qualified intern to conduct research on machine learning techniques for design optimization of electrical machines. The ideal candidate should have solid background and demonstrated research experience in mathematical optimization methods, especially in topology optimization and sensitivity analysis, as well as machine learning techniques. Hands-on experiences with the implementation of optimization algorithms, machine learning and deep learning methods are required. Knowledge and experience with electric machine principle, design and finite-element analysis is a strong plus. Senior Ph.D. students in related expertise are encouraged to apply. 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: Artificial Intelligence, Machine Learning, Optimization
    • Host: Bingnan Wang
    • Apply Now
  • MD1746: PWM inverter circuit design

    • MERL is looking for a self-motivated intern to work on PWM inverter drive circuit design and fabrication. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in power electronics. Experience in PWM inverter design, switching loss estimation, and EMI is desired. The intern is expected to collaborate with MERL researchers to design, simulate, and fabricate circuits, carry out experiments, analyze experimental data, and prepare manuscripts for scientific publications. The total duration is 3 months. This internship requires work that can only be done at MERL. This internship requires work that can only be done at MERL.

    • Research Areas: Control, Electric Systems, Signal Processing
    • Host: Dehong Liu
    • Apply Now
  • MD1697: Integrated design of mechatronic systems

    • MERL is seeking a highly motivated and qualified individual to conduct research in model-based mechatronic system design. The ideal candidate should have solid backgrounds in motor and drives, multi-body dynamics, design optimization, and coding skills. Demonstrated experience on hand-on mechatronic system integration, and simulation/optimization software such as Matlab is a necessity. Ph.D. students in mechanical engineering, robotics, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months. 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: Multi-Physical Modeling, Optimization, Robotics
    • Host: Yebin Wang
    • Apply Now
  • MD1696: Advanced RF Technologies

    • Mitsubishi Electric Research Laboratories (Cambridge, MA) is seeking a highly motivated, qualified individual to join our 3 month internship program of research on advanced RF technologies. The ideal candidate should be a senior Ph.D. student with good experience in microwave power amplifier/RF active circuit design and experiment, RF front end systems. Familiarity with ADS and Matlab is required. Knowledge of radio system architecture and FPGA (signal processing) would be an asset. 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: Communications, Electronic and Photonic Devices, Signal Processing
    • Host: Rui Ma
    • Apply Now
  • MD1561: Desgn and fabrication of power devices in power electronics or RF

    • MERL is seeking a highly motivated, qualified individual to join our 3-month internship program to carry out research in the area of power electronics and RF semiconductors devices. The ideal candidate should have a significant background in the simulation and design of a 2D and 3D GaN devices using Matlab and TCAD. Proficiency in device semiconductor modeling or hands-on experience in GaN device fabrication processes and a deep knowledge of negative capacitance would be a great asset. Candidates who hold a PhD or in their senior years of a Ph.D. program are encouraged to apply. 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: Electronic and Photonic Devices
    • Host: Koon Hoo Teo
    • Apply Now
  • MD1736: Data-driven fluid mechanics and control

    • MERL is seeking a highly motivated, qualified individual to join our internship program in the summer of 2022. The ideal candidate will be a senior Ph.D. student specializing in computer science, aerospace, mechanical, or applied mathematics. Research experience in computational fluid dynamics (CFD), C++ (OpenFOAM level), and Python (Keras w/ TensorFlow, PyTorch, etc.) is very desirable. Solid background in two or more of the following areas is required: Physics-Informed Neural Nets (PINNs), adjoint analysis, PDE-constrained optimization, reduced-order modeling (ROMs), statistical learning, parameter estimators, regression techniques, and probability theory. The starting date is flexible, and the internship will last 3-4 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary. 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: Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling
    • Host: Saleh Nabi
    • Apply Now
  • MD1648: THz Electronic Sensing

    • MERL is looking for a senior Ph.D. student to join our team to conduct application-motivated research and experiments. The candidate must have hands-on practical lab experiment experience on millimeter-wave, sub-THz, or THz for sensing, radar, and other applications. Skills of using RF/Microwave Lab equipment are necessary. Knowledge of solid-state device physics, high frequency, and high speed integrated circuit (IC) chip design, and signal processing is desired. The internship is expected to be 3-6 months, starting date is flexible after September. This internship requires work that can only be done at MERL.

    • Research Areas: Applied Physics, Computational Sensing, Electronic and Photonic Devices
    • Host: Rui Ma
    • Apply Now
  • MD1693: Aircraft electric propulsion system design

    • MERL is seeking a motivated and qualified individual to conduct research in modeling, simulation and analysis of aircraft electric propulsion system. The ideal candidate should have solid backgrounds in multi-physics modeling and simulation of aircraft electrical propulsion system. Demonstrated experience in modeling and simulation software/language such as Modelica or Simscape is a necessity. Knowledge and experience of NPSS, aircraft dynamics, and aerodynamics is a definite plus. Senior Ph.D. students in aerospace and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months. 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: Dynamical Systems, Electric Systems, Multi-Physical Modeling
    • Host: Yebin Wang
    • Apply Now
  • MD1714: Electric Motor Design

    • MERL is seeing a motivated and qualified individual to conduct research on electric machine design, prototype, and experiment tests. The ideal candidate should have solid background and demonstrated research experience in electric machine theory, design analysis, motor drives, and control. Hands-on experiences on electric motor design and prototyping, test bench set up, and experiment measurements are required. Senior Ph.D. students in electrical engineering or mechanical engineering with related expertise are encouraged to apply. Start date for this internship is flexible. This internship requires work that can only be done at MERL.

    • Research Areas: Applied Physics, Electric Systems, Multi-Physical Modeling
    • Host: Bingnan Wang
    • Apply Now
  • MD1300: Compiler Optimizations for Linear Algebra Kernels

    • MERL is looking for a highly motivated individual to work on automatic, compiler based techniques for optimizing linear algebra kernels. The ideal candidate is a Ph.D. student in computer science with extensive experience in compiler design and source code optimization techniques. In particular, the successful candidate will have a strong working knowledge of polyhedral optimization techniques, the LLVM compiler, and Polly. Strong C/C++ skills and knowledge of LLVM at the source level 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: Control, Machine Learning, Optimization
    • Host: Abraham Goldsmith
    • Apply Now
  • MD1715: Electric Motor Fault Analysis

    • MERL is seeing a motivated and qualified individual to conduct research on electric machine fault analysis and detection. The ideal candidate should have solid background in electric machine theory, modeling, numerical analysis, operation, and fault detection techniques, including machine learning. Research experiences on modeling and analysis of electric machines and fault detection are required. Hands-on experience with permanent magnet motor design and analysis, and knowledge on machine learning are desirable. Senior Ph.D. students in related expertise are encouraged to apply. Start date for this internship 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: Applied Physics, Machine Learning, Multi-Physical Modeling
    • Host: Bingnan Wang
    • Apply Now
  • MD1694: Path Planning for Articulated Vehicles

    • MERL is seeking a highly skilled and self-motivated intern to work on path/motion planning of articulated vehicles.

      The ideal candidate should have solid backgrounds in path/motion planning, nonlinear geometric control theory, and machine learning. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, electrical engineering, robotics, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 3 months. 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: Control, Machine Learning, Robotics
    • Host: Yebin Wang
    • Apply Now
  • CV1737: Robotic manipulation using vision and tactile sensing

    • The Computer Vision group at MERL is seeking a highly skilled graduate student for an internship position in Robotic Manipulation using Tactile Sensing and Machine Vision. The research work is expected to be disseminated in a scientific paper and published in one of the major robotics conferences. Candidates should have a solid understanding of contact mechanics, dexterous manipulation and point cloud processing. The intern will deploy the algorithms on physical robots. Strong programming skills are required, including MuJoCo, ROS, C++, Python. Duration and start dates are 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: Computer Vision, Machine Learning, Robotics
    • Host: Radu Corcodel
    • Apply Now
  • CV1723: Meta-Algorithmic Learning

    • The intern will work on the topic of designing deep neural networks for producing meta-algorithms for solving pre-defined tasks. A strong background in machine learning (especially in reinforcement learning) and computer vision is expected as demonstrated by top-tier publications. The candidate must be proficient in Python programming. Familiarity with optimization theory will be a plus. The selected intern will work with MERL researchers to conduct original research, as well as actively participate in preparing manuscripts. This internship requires work that can only be done at MERL.

    • Research Areas: Computer Vision, Machine Learning
    • Host: Anoop Cherian
    • Apply Now
  • CV1729: Next Generation Neural Networks

    • MERL is seeking an intern to conduct research in the area of novel neural network structures/architectures for applications in computer vision. This is an exploratory project to try to develop novel networks that offer capabilities beyond standard feedforward, convolutional networks. The ideal candidate is a PhD student with experience in deep learning and computer vision and a strong publication record at top-tier venues. Prior experience in the design of novel network architectures is desirable. Very 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: Machine Learning
    • Host: Mike Jones
    • Apply Now
  • CV1725: Few-Shot Action Recognition

    • MERL is looking for a self-motivated intern to work on problems at the intersection of video understanding and graph representation learning for solving few-shot action recognition problems. The ideal candidate would be a PhD student with a strong mathematical background in machine learning and computer vision and who has published at least one paper in a top-tier machine learning or computer vision venue (NIPS/CVPR/ECCV/ICCV/ICML/PAMI etc.). The candidate must have prior experience in using deep learning methods for video understanding (such as using Transformers) and self/unsupervised methods (such as contrastive learning). Proficiency in PyTorch is expected and familiarity with neural language models will be a plus. The intern will conduct original research with MERL researchers towards scientific publications. This internship requires work that can only be done at MERL.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Anoop Cherian
    • Apply Now
  • CV1774: Modeling uncertainty in computer vision

    • We seek a highly motivated intern to conduct original research in the estimation and modeling of uncertainty in deep-learning-based computer vision tasks. 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 TensorFlow, and broad knowledge of machine learning and deep learning methods are expected. Previous experience in uncertainty estimation and modeling is preferred.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Tim Marks
    • Apply Now
  • CV1724: Video Anomaly Detection

    • MERL is looking for a self-motivated intern to work on the problem of video anomaly detection. The intern will help to develop new ideas for improving the state of the art in detecting anomalous activity in videos. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision and some experience with video anomaly detection in particular. Proficiency in Python programming and Pytorch is necessary. The intern will collaborate with MERL researchers to develop and test algorithms and prepare manuscripts for scientific publications. The internship is for 3 months and the start date is flexible.

    • Research Areas: Computer Vision
    • Host: Mike Jones
    • Apply Now
  • CV1738: Robot autonomous grasping using tactile sensing

    • The Computer Vision group is offering an internship opportunity in robot autonomous grasping using tactile sensing. The internship is open to highly skilled graduate students on a PhD track. Candidates should have a solid understanding of reinforcement learning, contact mechanics, simulating contacts, grasping, pose estimation and point cloud processing. The policies will be deployed on physical robots and the sensing is provided by various types of tactile sensing arrays. Strong programming skills are required, including MuJoCo, ROS, C++ and Python. Duration and start dates are 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: Computer Vision, Machine Learning, Robotics
    • Host: Radu Corcodel
    • Apply Now
  • CV1721: Deep Learning for Robotic Grasping

    • MERL is looking for a highly motivated and qualified intern to work on computer vision for robotic grasping. The ideal candidate would be a Ph.D. student with a strong background in deep learning and robotics. There are several available topics for consideration including goal-driven grasping of novel objects, grasping for bin picking, and grasping in clutter. The project requires development of novel algorithms which can be implemented and evaluated on a robotic platform. Experience in working with a physics engine simulator like PyBullet, Mujoco, or Gazebo is preferred. Proficiency in Python programming is necessary and experience with ROS is a plus. Successful candidate will collaborate with MERL researchers and publication of the relevant results is expected. Start date is flexible and 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. 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: Computer Vision, Machine Learning, Robotics
    • Host: Siddarth Jain
    • Apply Now
  • CV1720: Computer Vision for Robotic Manipulation

    • MERL is looking for a highly motivated and qualified intern to work on computer vision for robotic manipulation. The ideal candidate would be a Ph.D. student with a strong background in computer vision, deep learning, and/or robotics. There are several available topics for consideration including task and motion planning (TAMP), learning for object manipulation, and pose estimation. The project requires development of novel algorithms which can be implemented and evaluated on a robotic platform. Experience in working with a physics engine simulator like PyBullet, Mujoco, or Gazebo is preferred. Proficiency in Python programming is necessary and experience with ROS is a plus. Successful candidate will collaborate with MERL researchers and publication of the relevant results is expected. Start date is flexible and 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. 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: Computer Vision, Machine Learning, Robotics
    • Host: Siddarth Jain
    • Apply Now
  • CV1568: Uncertainty Estimation in 3D Face Landmark Tracking

    • We are seeking a highly motivated intern to conduct original research extending MERL's work on uncertainty estimation in face landmark localization (the LUVLi model) to the domains of 3D faces and video sequences. 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 in computer vision and machine learning with a strong publication record. Experience in deep learning-based face landmark estimation, video tracking, and 3D face modeling is preferred. 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.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Tim Marks
    • Apply Now
  • CV1712: 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. 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: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Suhas Lohit
    • Apply Now
  • CV1770: Vital signs estimation using computer vision and machine learning

    • MERL is seeking a highly motivated intern to conduct original research in the area of monitoring vital signs such as heart rate, heart rate variability, breathing rate, 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/Tensorflow, Matlab, C/C++, etc.) are expected. 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: Computer Vision, Machine Learning, Signal Processing
    • Host: Tim Marks
    • Apply Now
  • CV1703: Software development in ROS for robotic manipulation

    • MERL is offering an internship position for non-research software development for robotic manipulation. The scope of the internship is to develop robust ROS packages by refactoring existing experimental code. The position is open to prospective candidates with very strong programming skills in ROS (Robot Operating System) using C++ primarily and Python respectively. The selected intern will have a software engineering role rather than research oriented. The position is open to both senior undergraduate students and master students. Flexible start and end dates. 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: Computer Vision, Data Analytics, Robotics
    • Host: Radu Corcodel
    • Apply Now
  • CV1701: Computer Vision for Biased or Scarce Data

    • MERL is looking for a self-motivated intern to work on data scarcity and bias issues for computer vision. The topics in the scope include (but not limited to): domain adaptation, generative modeling, transfer/low-shot/unsupervised learning, low-shot image/video anomaly localization, multi-model or multi-modal fusion or distillation under limited data, etc. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is expected 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. 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: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Kuan-Chuan Peng
    • Apply Now
  • CV1716: Cross-modal knowledge transfer

    • MERL is seeking an intern to conduct research in the area of cross-modal knowledge transfer such as RGB to IR. The focus will be on core computer vision applications like object detection and semantic segmentation for both images and videos. 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. 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: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Suhas Lohit
    • Apply Now
  • CV1773: Learning Neural Radiance Fields for Realistic Image Generation

    • MERL is seeking a highly motivated intern to conduct original research in data-driven realistic image generation that learns and combines implicit and explicit deep 3D models from unlabeled images. 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 with experience in implicit neural network models, GANs, and related deep learning methods, as well as good general knowledge in machine learning and a strong publication record. Previous experience with 3D face models and video generation is preferred. Strong programming skills in Python and flexibility working across various deep learning platforms (e.g., PyTorch and TensorFlow) are expected.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Tim Marks
    • Apply Now
  • CV1722: Multimodal Embodied AI

    • MERL is looking for a self-motivated intern to work on problems at the intersection of video understanding, audio processing, and language models. The ideal candidate would be a senior PhD student with a strong background in machine learning and computer vision (as demonstrated via top-tier publications). The candidate must have prior experience in developing deep learning methods for audio-visual-language data. Expertise in popular embodied AI 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. This internship requires work that can only be done at MERL.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
    • Host: Anoop Cherian
    • Apply Now
  • MS1769: Data-driven Dynamic Modeling of Vapor Compression Systems

    • MERL is seeking a highly motivated and qualified individual to conduct research in dynamic modeling and simulation of vapor compression systems in the summer of 2022. Knowledge of data-driven modeling techniques is required. Experience in working with thermo-fluid systems is preferred. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. Senior Ph.D. students in applied mathematics, chemical/mechanical engineering and other related areas are encouraged to apply. The expected duration of the internship is 3 months and the start date 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: Multi-Physical Modeling
    • Host: Hongtao Qiao
    • Apply Now
  • MS1717: Estimation and Optimization for Large-Scale Systems

    • MERL is seeking a motivated graduate student to research methods for state and parameter estimation and optimization of large-scale systems. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in control and estimation, numerical methods, and optimization; strong programming skills and experience with Julia/Python/Matlab are also expected. Knowledge of the fundamental physics of thermofluid flows (e.g., thermodynamics, heat transfer, and fluid mechanics), nonlinear dynamics, or equation-oriented languages (Modelica, gPROMS) is a plus. The expected duration of this internship is 3 months. 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: Control, Multi-Physical Modeling, Optimization
    • Host: Chris Laughman
    • Apply Now
  • MS1704: Probabilistic Machine Learning for Few-Shot Optimization

    • MERL is looking for a highly motivated and qualified candidate to work on probabilistic machine learning for few-shot optimization with real-world applications in building and energy systems. The ideal candidate will have a strong understanding machine learning with expertise demonstrated via, e.g., publications, in at least one of: few-shot/meta-learning methods, Bayesian optimization, multimodal learning, or learning for control/estimation of buildings and energy systems. Hands-on programming experience with standard ML toolkits such as PyTorch/Tensorflow is required; knowledge of additional, relevant tools (e.g., GPyTorch, Pyro) is a plus. PhD students are 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. 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: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization
    • Host: Ankush Chakrabarty
    • Apply Now
  • CA1726: Distributed Estimation for Autonomous Systems

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in developing estimation methods with applications to multi-vehicle positioning. The ideal candidate is a PhD candidate with strong emphasis in estimation and control, and as interest and background in several of: bayesian inference, machine learning, maximum-likelihood estimation, optimization, distributed systems, and vehicle modeling and control. Good programming skills in MATLAB, Python, or C/C++ are required. The expected start of of the internship is in 2022 and flexible for a duration of 3-6 months. 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: Control, Optimization, Signal Processing
    • Host: Karl Berntorp
    • Apply Now
  • CA1740: Locomotion of Legged Robots

    • MERL is looking for highly motivated interns at different levels of expertise to conduct research on robot locomotion of legged robots. The research spans multiple areas from modeling, motion planning, sensing and learning from data, to control. The ideal candidate will have experience in either one or multiple of the following topics: model predictive control, machine learning, numerical optimization, and optimal control. Good programming skills in MATLAB, ROS, Python, or C/C++ are required. Graduate students in robotics, engineering, or mathematics with a focus on legged robots, control theory, or numerical optimization are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. The expected duration of the internship is 3-6 months. The start date is flexible. This internship requires work that can only be done at MERL.

    • Research Areas: Control, Machine Learning, Robotics
    • Host: Marcel Menner
    • Apply Now
  • CA1707: Autonomous vehicles guidance and control

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control for autonomous vehicles. The research domain includes algorithms for path planning, vehicle control, high level decision making, sensor-based navigation, driver-vehicle interaction. The ideal candidate is expected to be working towards a PhD with strong emphasis in vehicle guidance and control, and to have interest and background in as many as possible of: vehicle dynamics modeling and control, predictive control algorithms linear and nonlinear systems, motion planning, convex, non-convex, and mixed -integer optimization, statistical estimation, cooperative control. Good programming skills in MATLAB, Python or C/C++ are required, knowledge of rapid prototyping systems, automatic code generation or ROS is a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months. 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: Control, Dynamical Systems, Optimization
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1742: Mixed-Integer Programming for Motion Planning and Control

    • MERL is looking for a highly motivated individual to work on tailored computational algorithms and applications of mixed-integer programming for decision making, motion planning and control of hybrid systems. The research will involve the study and development of numerical optimization techniques and/or the implementation and validation of 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: branch-and-bound type methods, heuristics for mixed-integer programming (pre-solve, cutting planes, warm starting, integer-feasible solutions), modeling and formulation of MIPs for hybrid control systems, convex and non-convex optimization, machine learning and real-time optimization. PhD students in engineering or mathematics, especially with a focus on mixed-integer programming or numerical optimization, are encouraged to apply. Publication of relevant results in conference proceedings and 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. 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: Control, Machine Learning, Optimization, Robotics
    • Host: Rien Quirynen
    • Apply Now
  • CA1706: Perception-aware vehicle control

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control algorithms accounting for perception of the uncertain surrounding environment. The ideal candidate is expected to be working towards a PhD with strong emphasis in control or planning algorithms, and to have interest and background in as many as possible of: predictive control algorithms for linear and nonlinear systems, stochastic constrained control, e.g., chance constraints, stochastic optimization, statistical estimation, perception system modeling, and vehicle modeling and control. Good programming skills in MATLAB, Python or C/C++ are required. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months. 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: Control, Optimization, Signal Processing
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1741: Learning for Connected Vehicles

    • MERL is seeking a highly motivated intern to collaborate with the Control for Autonomy team in the development of learning technologies for Connected Vehicles. The intern will conduct research in the development of methods for learning/optimization of Advanced Driver Assistance Systems (ADAS) using data-sharing between connected vehicles and/or infrastructure. The ideal candidate has knowledge of at least one of machine learning, estimation, connected vehicles, and vehicle control systems. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) is a plus. 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. The start date 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: Control, Dynamical Systems, Machine Learning
    • Host: Marcel Menner
    • Apply Now
  • CA1719: Spacecraft Guidance, Navigation, and Control

    • MERL is seeking highly motivated interns for research positions in guidance, navigation, and control of spacecraft. The ideal candidates have experience in one or more of the following topics: astrodynamics, the three-body problem, relative motion dynamics, rendezvous, attitude control, orbit control, orbit determination, nonlinear estimation, and optimization-based control. PhD students in aerospace, mechanical, or electrical engineering are encouraged to apply. Publication of results produced during the internship is expected. The duration of the internships are 3-6 months, and the start dates are 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: Control, Dynamical Systems
    • Host: Avishai Weiss
    • Apply Now
  • CA1705: Fault-tolerant planning and control of autonomous systems

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on fault-tolerant algorithms for planning and control of autonomous vehicles. The ideal candidate is expected to be working towards a PhD with strong emphasis in control or planning algorithms, and to have interest and background in as many as possible of: invariance and set-based control, decision making, predictive control algorithms for linear and nonlinear systems, formal methods for control, and vehicle modeling and control. Good programming skills in MATLAB, Python or C/C++ are required. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months. 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: Control, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1728: Safe data-driven control of dynamical systems under uncertainty

    • MERL is looking for a highly motivated individual to work on safe control of data-driven, uncertain, dynamical systems. The research will develop novel optimization and learning-based control algorithms to guarantee safety and performance in various industrial applications, including autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: optimal control under uncertainty, (robust and stochastic) model predictive control, (convex and non-convex) optimization, and (reinforcement and statistical) learning. Ph.D. students in engineering or mathematics with a focus on control, optimization, and learning are encouraged to apply. A successful internship will result in submission of relevant results to peer-reviewed conference proceedings and journals, and development of well-documented (Python/MATLAB) code for MERL. The expected duration of the internship is 3-6 months, and the start date is Summer 2022. 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: Artificial Intelligence, Control, Dynamical Systems, Optimization, Robotics
    • Host: Abraham P. Vinod
    • Apply Now
  • CA1731: Motion planning and control of multi-agent systems

    • MERL is looking for a highly motivated individual to develop planning and control algorithms for multi-agent systems. The internship will also include experimental validation of the proposed algorithms in various robotic testbeds (quadrotors and mini-cars) at MERL. The ideal candidate is experienced in multi-agent motion planning and control, and has successfully demonstrated some of their prior work on hardware testbeds. The candidate must be proficient in ROS and C/C++, and at least familiar with Python and MATLAB. Prior experience with crazyflies and/or hamster robots will be considered a plus. The expected duration of the internship is 3-6 months, and the start date is Summer/Fall 2022. 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: Control, Dynamical Systems, Optimization, Robotics
    • Host: Abraham P. Vinod
    • Apply Now
  • CA1727: Learning for Control

    • MERL is looking for highly motivated interns to work with the Control for Autonomy team in the domain of data-based estimation for integration into control, with applications to, e.g., vehicle control. The ideal candidate is working towards a PhD with emphasis on control and has experience in as many as possible of the following topics: statistical signal processing, Bayesian inference, predictive control, stochastic constrained control, statistical learning. 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 in 2022 but 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: Control, Machine Learning, Signal Processing
    • Host: Karl Berntorp
    • Apply Now
  • CA1743: Coordination of Connected and Automated Vehicles

    • MERL is seeking a highly motivated qualified intern to collaborate with the Control for Autonomy team in the development of optimization-based coordination of connected and automated vehicles. The intern will conduct research in the development of methods for multi-vehicle coordination and/or focus on the implementation and validation in realistic scenarios. The ideal candidate should have experience in either one or multiple of the following topics: formulation of mixed-logic constraints as mixed-integer programs, control synthesis from Temporal Logic specifications, connected vehicles and coordination, and vehicle control systems. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) is a plus. 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. 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: Control, Dynamical Systems, Optimization, Robotics
    • Host: Rien Quirynen
    • Apply Now
  • SP1747: Learning for Inverse Problems

    • The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that solve inverse problems in computational sensing that incorporate deep learning architectures for a variety of sensing applications. The project goal is to improve the performance and develop an analysis of algorithms used for inverse problems by incorporating new tools from machine learning and artificial intelligence. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, large-scale optimization, plug-and-play priors, learning-based modeling for imaging, learning theory for computational imaging. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date 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: Applied Physics, Computational Sensing, Machine Learning, Optimization, Signal Processing
    • Host: Hassan Mansour
    • Apply Now
  • SP1748: Learning-based Wireless Sensing

    • The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in learning-based wireless sensing using communication signals (e.g., Wi-Fi) and other RF signals (such as millimeter-wave sensing waveforms). Expertise in deep learning in one of the following areas: localization, occupancy sensing, device-free pose/gesture recognition, skeleton tracking, and multi-modal fusion, is highly preferred. Familiarity with IEEE 802.11 (g/n/ac/ad/ay)standards 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 using MERL in-house testbed, and prepare results for publication. The expected duration of the internship is 3 months with a flexible start date. This internship requires work that can only be done at MERL.

    • Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal Processing
    • Host: Perry Wang
    • Apply Now
  • SP1734: Robust Machine Learning

    • MERL is seeking a highly motivated and qualified intern to work on robust machine learning techniques. The intern will collaborate with MERL researchers on developing novel approaches to address the problem of adversarial examples. The ideal candidate would have research experience in robust machine learning methods and defenses against adversarial examples. 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 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: Artificial Intelligence, Machine Learning, Signal Processing
    • Host: Ye Wang
    • Apply Now
  • SP1468: 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, Qiskit, and PennyLane will be additional assets to this position. 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: Artificial Intelligence, Machine Learning, Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • SP1709: Advanced Networking Technologies

    • MERL is seeking a highly motivated and qualified intern to join the Signal Processing Group for a three month internship program. The ideal candidate will be expected to carry out research on emerging IoT networking. The candidate is expected to develop innovative networking technologies to achieve efficient network traffic delivery. The candidates should have knowledge of networking protocols such as multi-path TCP/UDP and RPL. Knowledge of the data protection such as erasure coding is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. 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: Control, Electric Systems, Signal Processing
    • Host: Jianlin Guo
    • Apply Now
  • SP1752: Machine Learning for Electric Design Automation

    • MERL is seeking a highly motivated and qualified intern to join the Signal Processing group for an internship program. The ideal candidate will be expected to carry out research on machine learning for automated design synthesis to improve hardware efficiency of various digital signal processing algorithms. The candidate is expected to have solid knowledge of deep learning, reinforcement learning, symbolic learning, decision making, and graph neural networks. Hands-on experience of high-level synthesis, FPGA prototyping, verilog, and general digital signal processing is a plus. 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: Artificial Intelligence, Electric Systems, Machine Learning, Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • SP1753: Algorithms for Coherent Imaging Systems

    • MERL is seeking an intern to work on estimation algorithms for coherent optical imaging. The ideal candidate would be a senior PhD student working in coherent imaging. The candidate should have experience in statistical modeling and estimation.

      A detailed knowledge of optical interferometry and imaging with a focus on either optical coherence tomography, optical coherence microscopy or FMCW LIDAR is also preferred. Strong programming skills in MATLAB or Python are essential. Publication of the results produced during our internships is expected. Duration is anticipated to be 3 to 6 months. 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: Computational Sensing, Electronic and Photonic Devices, Signal Processing
    • Host: Joshua Rapp
    • Apply Now
  • SP1718: Brain-Machine Interface

    • 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, deep learning, mixed reality, bio sensors and sensing as well as signal processing. 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: Artificial Intelligence, Machine Learning, Robotics, Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • SP1749: Radar-Assisted Automotive Perception

    • The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in radar-assisted automotive perception. Expertise in deep learning-based object detection, multiple (extended) object tracking, data association, and motion/measurement model learning is required. Previous hands-on experience on open automotive datasets is a plus. Familiarity with the concept of FMCW, MIMO, and range-Doppler-angle spectrum is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date. This internship requires work that can only be done at MERL.

    • Research Areas: Artificial Intelligence, Computational Sensing, Dynamical Systems, Machine Learning, Signal Processing
    • Host: Perry Wang
    • Apply Now
  • SP1732: AI Security for Cyber Physical Systems

    • MERL is seeking a highly motivated, qualified intern to join a 3 month internship program. The ideal candidate will be expected to carry out research on AI security for various cyber physical systems. The candidate is expected to develop innovative AI technologies to increase cyber security. Candidates should have strong knowledge about neural network and learning techniques, such as feature extraction, machine learning, explainable learning, domain adaptation, robust learning, and distributed learning. Proficient programming skills with PyTorch, and strong mathematical analysis will be additional assets to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. 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: Artificial Intelligence, Communications, Data Analytics, Machine Learning, Signal Processing
    • Host: K.J. Kim
    • Apply Now
  • SP1730: Advanced Signal Processing for RF-controlled metasurface

    • MERL is seeking a highly motivated, qualified intern to carry out research on Advanced Signal Processing for RF-controlled meta-surfaces. The candidate is expected to develop innovative signal processing for RF-controlled meta-surfaces aiding various applications. Candidates should have strong knowledge of machine learning, channel estimation, beamforming, interference mitigation, optimization, and electromagnetic field analysis. Proficient programming skills with Python and MATLAB and strong mathematical analysis will be additional assets to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. The expected duration of the internship is 3-6 months, with a flexible start date. 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: Communications, Machine Learning, Optimization, Signal Processing
    • Host: K.J. Kim
    • Apply Now
  • SP1733: ML for GNSS-based Applications

    • MERL is seeking a highly motivated, qualified intern to work on machine learning for Global Navigation Satellite System (GNSS) applications. The ideal candidate is working towards a PhD and is expected to develop innovative machine learning technologies to increase accuracy and integrity of GNSS-based positioning systems. Candidates should have strong knowledge about as many as possible of GNSS signal processing for multipath mitigation, handling RINEX data, neural network and learning techniques, such as feature extraction, deep machine learning, reinforcement learning, domain adaptation, and distributed learning. Proficient programming skills with PyTorch, Matlab, and C++, and strong mathematical analysis will be additional assets to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. 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: Communications, Dynamical Systems, Machine Learning, Signal Processing
    • Host: K.J. Kim
    • Apply Now
  • SP1762: Computational Sensing Technologies

    • The Computational Sensing team at MERL is seeking motivated and qualified individuals to assist in the development of computational methods for a variety of sensing applications. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, deep learning for inverse problems, large-scale optimization, blind inverse scattering, radar/lidar/THz imaging, joint communications and sensing, multimodal sensor fusion, object or human tracking, sensing of dynamical systems, or wave-based inversion. Experience with experimentally measured data is desirable. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.

    • Research Areas: Computational Sensing, Signal Processing
    • Host: Petros Boufounos
    • Apply Now
  • SP1763: Technologies for Multimodal Tracking and Imaging

    • MERL is seeking a motivated intern to assist in developing hardware and algorithms for multimodal imaging applications. The project involves integration of radar, camera, and depth sensors in a variety of sensing scenarios. The ideal candidate should have experience with FMCW radar and/or depth sensing, and be fluent in Python and scripting methods. Familiarity with optical tracking of humans and experience with hardware prototyping is desired. Good knowledge of computational imaging and/or radar imaging methods is a plus. 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: Computational Sensing, Signal Processing
    • Host: Petros Boufounos
    • Apply Now
  • SP1750: 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. This internship requires work that can only be done at MERL.

    • Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Optimization, Signal Processing
    • Host: Perry Wang
    • Apply Now
  • SP1711: Advanced Network Design

    • MERL is seeking a highly motivated and qualified intern to join the Signal Processing Group for a three month internship program. The ideal candidate will be expected to carry out research on network design and optimization methods including AI assisted networking. The candidate is expected to develop innovative network configuration technologies to support emerging IoT applications. The candidates should have knowledge of network technologies such as network slicing, software defined networking and/or semantic networking. Knowledge of the communication technologies such as 3GPP-5G or IEEE 802 WLAN/WPAN standards is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Communications, Control, Optimization
    • Host: Jianlin Guo
    • Apply Now
  • SP1710: Distributed Learning and Computing over Networks

    • MERL is seeking a highly motivated and qualified intern to join the Signal Processing Group for a three month internship program. The ideal candidate will be expected to carry out research on collaborative learning between infrastructure, devices, and vehicles. The candidate is expected to develop distributed learning for various applications, including autonomous driving, smart infrastructure, mobile networks, etc.. The candidate should have knowledge of federated learning and distributed computing, networking and over-the-air-computation. Knowledge of scheduling, spectrum management, and mathematical analysis for convergence testing is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Communications, Machine Learning, Optimization
    • Host: Jianlin Guo
    • Apply Now
  • DA1751: Power Grid Fault Event Detection

    • MERL is seeking a highly motivated and qualified individual to join our summer internship program and conduct research in the area of power grid fault event detection. The ideal candidate should have a solid knowledge of power grid, inverter control and protection, outage analysis, signal processing, and machine learning. Experience with MATLAB or C/C++/Python is required. The duration of the internship is expected to be 3-6 months, and the start date is flexible. Candidates in their senior or junior years of a Ph.D. program are encouraged to apply. 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: Data Analytics, Electric Systems, Machine Learning, Signal Processing
    • Host: Hongbo Sun
    • Apply Now
  • DA1677: Machine Learning Algorithms for Sequence Prediction

    • MERL is looking for a highly motivated and qualified candidate to work on machine learning algorithms for prediction of spatiotemporal data represented as time series of geospatial locations. The ideal candidate will have solid understanding of sequence prediction algorithms, including transformer neural networks, recurrent neural networks, and other deep neural network models, as well as good foundational knowledge of discrete event systems, including Markov and semi-Markov models. Demonstrated hands-on experience with PyTorch or other Python implementations of such algorithms is required. Additional knowledge of time series analysis and statistical machine learning, as well as experience with tools and methods for geospatial processing would be a plus. PhD students are preferred, but Master's students will be considered, too. The expected duration of the internship is 3-4 months. 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: Data Analytics, Machine Learning
    • Host: Daniel Nikovski
    • Apply Now
  • DA1768: Contact Modeling and Optimization

    • MERL is looking for a self-motivated and qualified candidate to work on modeling for contact phenomenon. Robotic manipulation is heavily affected by external contacts that can be modeled with physical and data driven models. We are interested in researching those models for analysis and control purposes. The ideal candidate is a PhD student and should have experience and records in multiple of the following areas. Contact modeling and robotic manipulation. Physic Engines like Mujoco, Bullet, Drake and sim2real gap problems. Machine learning techniques for modeling and control such as Gaussian Processes and Neural Networks. Experience in working with robotic systems. Knowledge in learning from demonstration algorithms and standard Reinforcement Learning algorithms is a plus. Proficiency in Python is required. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms that will lead to a scientific publication. Typical internship length is 3-4 months. 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: Artificial Intelligence, Dynamical Systems, Machine Learning, Robotics
    • Host: Diego Romeres
    • Apply Now
  • DA1708: Microgrid fault detection and localization

    • MERL is seeking a highly motivated and qualified individual to join our internship program and conduct research in the area of microgrid fault detection and localization. The ideal candidate should have a solid knowledge of microgrids, inverter control, fault detection and localization, power system protection, and signal processing. Experience with MATLAB, Simulink, and Simscape Electrical is required. The expected duration of the internship is 3 months. Ph.D. students are preferred, but Master''s students will also be considered. 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: Data Analytics, Electric Systems
    • Host: Hongbo Sun
    • Apply Now
  • DA1713: Resilient Power Grid and Multi-Energy Systems

    • MERL is seeking a highly motivated and qualified individual to join our summer internship program and conduct research in the area of resilient power grid and multi-energy systems. The ideal candidate should have a solid knowledge of power systems, gas network, water network, energy hubs, and optimization. Experience with MATLAB or C/C++/Python is required. The duration of the internship is expected to be 3-6 months, and the start date is flexible. Candidates in their senior or junior years of a Ph.D. program are encouraged to apply 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: Data Analytics, Electric Systems
    • Host: Hongbo Sun
    • Apply Now
  • DA1766: Time Series Analytics and Deep Learning

    • MERL is looking for a self-motivated intern to develop algorithms for time series data analytics, stochastic process modeling, and deep learning. The ideal candidate would be a senior PhD student with experience in one or more of the following areas: machine/deep learning, mathematical optimization, discrete-event systems modeling. Strong programming skills using C# and Python/PyTorch are expected. Experience in transportation applications and spatial-temporal predictions would be a plus. The intern is expected to work with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is expected to be 3 months. Start date 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: Artificial Intelligence, Data Analytics, Machine Learning, Optimization
    • Host: BinBin Zhang
    • Apply Now
  • DA1702: Machine Learning for Robotic Manipulation

    • MERL is looking for a self-motivated and qualified candidate to work on robotic manipulation projects. The ideal candidate is a PhD student and should have experience and records in one or multiple of the following areas. 1) Machine learning techniques for modeling and control such as Gaussian Processes and Neural Networks. 2) Knowledge of standard Reinforcement Learning algorithms. 3) Experience in working with robotic systems and familiarity with one physics engine simulator like Mujoco, pyBullet, pyDrake. 4) Optimization-based control for complementarity systems. Proficiency in Python is required. The successful candidate will be expected to develop, in collaboration with MERL employees, state of the art algorithms to solve complex robotic manipulation tasks that will lead to a scientific publication. Typical internship length is 3-4 months. 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: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Optimization, Robotics
    • Host: Diego Romeres
    • Apply Now
  • DA1508: Safe reinforcement learning for real-life applications

    • MERL is seeking a motivated and qualified individual to conduct research in safe 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. Publication of the results produced during the internship is anticipated, e.g., ICML, ICLR, NeurIPS. Duration of the internship is expected to be 3 months. Start date is flexible.

    • Research Areas: Artificial Intelligence
    • Host: Mouhacine Benosman
    • Apply Now