Internship Openings

43 / 65 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, Sudan 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

The COVID pandemic has impacted every aspect of life-how we live, work, and interact. At MERL, we are committed to maintaining our internship program through these challenging times.

MERL continues to actively seek candidates for research internships -- some of the posted positions are immediately available, while others target the summer of 2021. Please consider applying for positions of interest. Our researchers will follow up to schedule an interview by phone or video conference for qualified candidates.

Due to the situation with the COVID-19 pandemic, our current internships are mostly remote. Next summer we hope the situation will be better and our internships will be at MERL, but if it is not, most internships will continue to be remote. However, some of the internships require onsite work. Please check for any specific requirements for onsite work in the job description.


  • SA1573: Design and simulation of metasurface optics using deep learning

    • MERL is seeking a highly motivated, qualified individual to join our internship program and conduct research in the area of metasurface optic device simulation and design using deep learning. The ideal candidate should have a strong background in the simulation (such as Lumerical FDTD or open-source equivalents), design, and testing of metasurface optics, as well as hands-on experience in deep learning (such as autoencoders and GANs using Tensorflow/Keras/PyTorch). Experience in related fields (silicon photonics, plasmonics, optimization algorithms, machine learning, etc.,) would be considered a plus. Candidates who hold a Ph.D. or are in their senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Electronic and Photonic Devices, Machine Learning, Optimization
    • Host: Matt Brand
    • Apply Now
  • SA1469: Audio source separation and sound event detection

    • We are seeking multiple graduate students interested in helping advance the fields of source separation, speech enhancement, 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 audio signal processing, microphone array processing, probabilistic modeling, and deep learning techniques requiring minimal supervision (e.g., unsupervised, weakly-supervised, self-supervised, or few shot learning). The expected duration of the internship is 3-6 months and start date is flexible.

    • Research Areas: Machine Learning, Speech & Audio
    • Host: Gordon Wichern
    • Apply Now
  • MD1564: Data-driven fluid mechanics and control

    • The Muti-Physics and Dynamics (MD) group at MERL is seeking a highly motivated, qualified individual to join our internship program in the summer of 2021. The ideal candidate will be a senior Ph.D. student specializing in fluid mechanics, control, turbulence modeling, reduced-order modeling, and non-convex optimization. Research experience in computational fluid dynamics (CFD), data-assimilations, continuous and discreet adjoint methods is highly desirable. Familiarity with computational programming languages like Python, Fortran or C++ (openFOAM level) is expected. Publication of results obtained during the internship is expected. The starting date is flexible between April-June 2021, 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, Optimization
    • Host: Saleh Nabi
    • Apply Now
  • MD1505: Machine Learning for Microwave Circuit Intelligent Design

    • MERL is looking for a highly motivated, and qualified individual to join our internship program of exploring machine learing for microwave circuit intelligent design research. The ideal candidate should be a senior Ph.D. student with rich experience in machine learning/reinforcement learning. Knowledge of optimization, RF/Microwave integrated circuits, stochastic signal processing, and python programming skills are required. Duration is 3-6 months with a flexible start date.

    • Research Areas: Artificial Intelligence, Electronic and Photonic Devices, Machine Learning
    • Host: Rui Ma
    • 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
  • MD1593: Design Optimization for Electric Machines

    • MERL is seeking a motivated and qualified intern to conduct research on design optimization of electrical machines. The ideal candidate should have solid background and demonstrated research experience in mathematical optimization methods, especially in topology optimization, robust optimization, sensitivity analysis, and machine learning techniques. Hands-on experiences with the implementation of optimization algorithms, machine learning and deep learning methods are highly desirable. 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. Start date for this internship is flexible and the duration is about 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: Artificial Intelligence, Machine Learning, Multi-Physical Modeling, Optimization
    • Host: Bingnan Wang
    • Apply Now
  • MD1558: Symbolic regression

    • MERL is seeking a self-motivated intern to conduct fundamental research in the area of symbolic regression and deep learning for applications of recovering mathematical expressions or physical laws. The ideal candidate would be a senior PhD student with solid background in machine learning and strong publication record in top-tier venues. Prior experience in symbolic regression is strongly preferred. Very good Python, Pytorch/Tensorflow, and Matlab skills 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 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: Dehong Liu
    • Apply Now
  • MD1377: Adaptive Optimal Control of Electrical Machines

    • MERL is seeking a motivated and qualified individual to conduct research in control of electrical machines. The ideal candidate should have solid backgrounds in adaptive dynamic programming and state/parameter estimation for electrical machines, demonstrated capability to publish results in leading conferences/journals, and experience with real-time control experiments involving high power devices. Senior Ph.D. students are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

    • Research Areas: Control, Electric Systems, Machine Learning
    • Host: Yebin Wang
    • Apply Now
  • DA1533: 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 multiple of the following areas. Machine learning techniques for modeling and control such as Gaussian Processes and Neural Networks. Knowledge of standard Reinforcement Learning algorithms. Experience in working with robotic systems and familiarity with one physics engine simulator like Mujoco, pyBullet, pyDrake. 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, Machine Learning, Robotics
    • Host: Diego Romeres
    • Apply Now
  • SP1512: Mutual Interference Mitigation

    • The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in mutual interference mitigation for automotive radar. Previous experience in waveform design, radar detection under interference, joint communication and sensing, interference mitigation, and deep learning for radar is highly preferred. Knowledge about automotive radar schemes (MIMO and waveform modulation, e.g., FMCW, PMCW, and OFDM) is a plus. 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 patents and publication. Senior Ph.D. students with research focuses on signal processing, machine learning, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 3 months with a flexible start date.

    • Research Areas: Artificial Intelligence, Communications, Computational Sensing, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Signal Processing
    • Host: Perry 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. Also note that we wish to fill this position as soon as possible and expect that the candidate will be available during this fall/winter. 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
  • SP1582: Source & Channel Coding

    • MERL is seeking a highly motivated, qualified individual to join our internship program of research on applied coding for data science. The ideal candidate is expected to possess an excellent background in channel coding, source coding, information theory, coding theory, coded modulation design, signal processing, deep learning, quantum computing, and molecular computing. 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, Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • SP1513: Designing and optimizing photonic devices using deep learning

    • MERL is seeking a highly motivated, qualified individual to join our internship program and conduct research in the area of photonic and nanophotonic device design and optimization using deep learning. The ideal candidate should have a strong background in the simulation (such as Lumerical FDTD), design, and testing of devices for optical communications and/or optical computing, as well as hands-on experience in deep learning (such as autoencoders and GANs using Tensorflow/Keras/PyTorch). Experience in silicon photonics, photonic crystal, plasmonicss, metasurface optics, optimization algorithms, machine learning, quantum computing, photonic device fabrication/measurements, and mask designs for InP and silicon photonic MPW would be considered an asset. Candidates who hold a Ph.D. or in their senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Applied Physics, Electronic and Photonic Devices, Machine Learning
    • Host: Keisuke Kojima
    • Apply Now
  • SP1478: Intelligent Brain-Machine Interface

    • MERL is seeking an intern to work on research for man-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, and 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, Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • SP1506: 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 (such as WiFi, Bluetooth, 5G) and other RF signals (such as FMCW). Previous experience in occupancy sensing, people counting, localization, device-free pose/gesture recognition, and skeleton tracking with deep learning is highly preferred. Familiarity with IEEE 802.11 (g/n/ac/ad/ay)standards is a plus. 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. Senior Ph.D. students with research focuses on wireless communications, machine learning, signal processing, optimization, applied mathematics, or related areas are encouraged to apply. The expected duration of the internship is 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, Communications, Computational Sensing, Dynamical Systems, Machine Learning, Robotics, Signal Processing
    • Host: Perry Wang
    • Apply Now
  • SP1537: Machine Learning for Wireless Communications

    • MERL is seeking an intern to work on machine learning for wireless communication systems. The ideal candidate would be an experienced PhD student or post-graduate researcher working in wireless communications with a focus on machine learning methods. The candidate should have a detailed knowledge of wireless communications, with some experience in machine learning, MIMO, and/or channel equalization preferred. Strong programming skills in Python and machine learning frameworks are essential. The expected duration of the internship is 3-6 months with flexible start date and length. 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, Machine Learning
    • Host: Ye Wang
    • Apply Now
  • SP1510: Learning for inverse problems and dynamical systems

    • 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, and Koopman theory/dynamic mode decomposition. 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: Computational Sensing, Dynamical Systems, Machine Learning, Signal Processing
    • Host: Hassan Mansour
    • Apply Now
  • SP1551: Algorithms for Large-Scale Optimal Transport

    • The Computational Sensing team at MERL is seeking motivated individuals to develop scalable optimal transport algorithms. Ideal candidates should be Ph.D. students with research experience in optimal transport and scalable optimal transport algorithms. Experience with GPU implementations is a plus. Publication of the results produced during our internships is expected. The duration of the internships is anticipated 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: Computational Sensing, Computer Vision, Machine Learning, Optimization, Signal Processing
    • Host: Yanting Ma
    • Apply Now
  • SP1585: Three dimensional Imaging from Compton Camera

    • The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that reconstruct a three dimensional distribution of a radioactive source when observed using a Compton camera. The project goal is to improve the performance and develop an uncertainty analysis of these algorithms. Ideal candidates should be Ph.D. students and have solid background and publication record in 3D Compton imaging. Experience in computational tomography, imaging inverse problems, and large-scale optimization is also preferred. 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, Computer Vision, Machine Learning, Optimization, Signal Processing
    • Host: Hassan Mansour
    • Apply Now
  • SP1522: AI Security for Cyber Physical Systems

    • MERL is seeking a highly motivated, qualified intern to join a thirteen weeks 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, 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, Machine Learning, Signal Processing
    • Host: K.J. Kim
    • Apply Now
  • SP1517: AI-based spectrum management for 5G wireless networks and beyond

    • MERL is seeking a highly motivated, qualified intern to join a thirteen weeks internship program. The ideal candidate will be expected to carry out research on emerging 5G wireless networks and beyond for industrial applications. The candidate is expected to develop innovative spectrum-based traffic recognition and optimal scheduling for local spectrum access. Candidates should have strong knowledge about 5G networks, spectrum management, cognitive radio, and neural network. Proficient programming skills with MATLAB, C++, Python (Pytorch), experience with ns-3 simulator, 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, Machine Learning, Signal Processing
    • Host: K.J. Kim
    • Apply Now
  • CA1519: Estimation for High-Precision Positioning

    • MERL is seeking a highly motivated candidate for development of next-generation high-precision positioning methods for autonomous systems applications, e.g., autonomous driving. The candidate will work with the Control for Autonomy team and the Signal Processing group in developing satellite-based positioning methods using information from multiple sources. Previous experience with at least some of the Bayesian inference, distributed estimation, satellite navigation systems, is highly desirable. Solid knowledge in MATLAB is required, working experience in C/C++ is desired, and previous experience with satellite navigation packages such as RTKLib is a merit. PhD candidates meeting the above requirements are encouraged to apply. The expected duration of the internship is 3-6 months with 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: Control, Machine Learning, Signal Processing
    • Host: Karl Berntorp
    • Apply Now
  • CA1518: Safe control of data-driven, uncertain systems

    • 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 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, (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 the submission of relevant results to peer-reviewed conference proceedings and journals, and the development of well-documented (Python/MATLAB) code for MERL. 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, Machine Learning, Optimization
    • Host: Abraham P. Vinod
    • Apply Now
  • CA1529: Energy Management for Electric Vehicles

    • MERL is looking for a highly motivated intern to conduct research on data-driven energy management strategies for (hybrid) electric vehicles. The candidate will develop methods that use data, e.g., of human drivers or traffic conditions, in order to improve the control of electric vehicles. The ideal candidate will have experience in either one or multiple of the following topics: model predictive control, machine learning, statistical learning, numerical optimization, and (inverse) optimal control. Prior experience with (hybrid) electric vehicles is a plus. Good programming skills in MATLAB, Python, or C/C++ are required. PhD students in engineering or mathematics with a focus on 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 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
  • CA1521: Coordinated Perception and Control for Autonomous Systems

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of algorithms for coordinating control and perception in autonomous systems. The overall objective is to determine the sensing strategy together with the motion/control strategy to effectively achieve a control goal while managing the risk due to the environment uncertainty. 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 among: predictive control, stochastic tubes, scenario-based stochastic optimization, uncertainty and risk representation, machine learning and motion planning algorithms. Good programming skills in MATLAB and/or Python, are required. The expected duration of the internship is in the Spring of 2021, 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, Machine Learning, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1515: Mixed-Integer Programming for Hybrid Control

    • MERL is looking for a highly motivated individual to work on tailored computational algorithms and applications of mixed-integer programming for decision making, 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 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
  • CA1528: Learning-Based Stochastic Predictive Control

    • MERL is looking for highly motivated interns to work in the domain of data-based controller design and algorithms for stochastic model predictive control (MPC) methods. The research involves the derivation, implementation, and validation of novel algorithms for optimization-based/data-driven control for industrial applications, e.g., related to autonomous driving and robotics. The ideal candidate has experience in either one or multiple of the following topics: stochastic MPC (e.g., scenario trees or tube MPC), (inverse) optimal control, convex and nonconvex optimization, parallel processing, real-time optimization, machine learning, statistical learning, and Bayesian inference. PhD students in engineering or mathematics, especially with a focus on stochastic and learning-based control or numerical optimization, 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. 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
    • Host: Karl Berntorp
    • Apply Now
  • CA1565: Connected Vehicle Driver Assistance Systems

    • MERL is seeking a highly motivated qualified intern to collaborate with the Control for Autonomy team and the Signal Processing group in the development of Advanced Driver Assistance Systems (ADAS) for Connected Vehicles. The intern will collaborate in the development of methods for distributed learning and optimization of ADAS using data-sharing between connected vehicles and infrastructure. The ideal candidate has knowledge of machine learning, optimization and connected vehicles. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) is a plus. Good programming skills in MATLAB, Python, or C/C++ are required. 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, starting in Spring or Summer 2021, but later starting periods may 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: Control, Machine Learning, Optimization
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1531: Learning-based multi-agent motion planning

    • MERL is seeking a highly motivated intern to research multi-agent motion planning by combining optimization-based methods with machine learning. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Robotics, Computer Science or related program, with prior experience in multi-agent motion planning, machine learning (especially supervised, reinforcement, and safe ML), and convex and non-convex optimization. A successful internship will result in innovative methods for multiagent planning, in the development of well-documented (Python/MATLAB) code for validating the proposed methods, and in the submission of relevant results for publication in peer-reviewed conference proceedings and journals. The expected duration of the internship is 3 months with a flexible start date in the Spring/Summer 2021. 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, Optimization, Robotics
    • Host: Abraham P. Vinod
    • Apply Now
  • CV1535: Exploring Kervolutional Neural Networks

    • MERL is seeking an intern to conduct research in the area of neural networks with nonlinear kernel activation functions (kervolutional networks) for applications in computer vision. 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 and knowledge of kervolutional networks is strongly preferred. 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. 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
    • Host: Mike Jones
    • Apply Now
  • CV1570: Health monitoring from video

    • MERL is seeking a highly motivated intern to conduct original research in the area of monitoring vital signs, such as heart rate and heart rate variability, from video of a person. The successful candidate will collaborate with MERL researchers to derive and implement new models, collect data, conduct experiments, and prepare results for publication. 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, Matlab, C/C++, etc.) are expected.

    • Research Areas: Computer Vision, Machine Learning, Signal Processing
    • Host: Tim Marks
    • Apply Now
  • CV1569: Robot learning from videos of human demonstrations

    • MERL is looking for a highly motivated and qualified intern to work on developing algorithms for robot learning from videos of human demonstrations. The ideal candidate would be a current Ph.D. student with a strong background in computer vision, deep learning, and robotics. Familiarity with imitation learning, learning from demonstrations (LfD), reinforcement learning, and machine learning for robotics will be valued. Proficiency in Python programming is necessary and experience in working with a physics engine simulator like Mujoco or pyBullet is a plus. A successful candidate will collaborate with MERL researchers and publication of the relevant results is expected. 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. 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, Robotics
    • Host: Jeroen van Baar
    • Apply Now
  • CV1579: Pose Estimation for Robotic Manipulation

    • MERL is looking for a highly motivated and qualified intern to work on developing algorithms for estimating pose for robotic manipulation. The ideal candidate would be a current Ph.D. student with a strong background in computer vision, deep learning, and robotics. Familiarity with machine learning/AI is required, and familiarity with reinforcement learning will be valued. Proficiency in Python programming is necessary and experience in working with a physics engine simulator like Mujoco or pyBullet is a plus. A successful candidate will collaborate with MERL researchers and publication of the relevant results is expected. 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. 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, Robotics
    • Host: Jeroen van Baar
    • 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
  • CV1553: Graph Representations for 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 action recognition problems. The ideal candidate would be a senior year (>=3) 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 action recognition, scene graph representations, etc.) and language models (such as in visual question answering or captioning). Proficiency in Python and flexibility in using different deep learning software (such as Pytorch) is expected. The internship is for 3 months with 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: Anoop Cherian
    • Apply Now
  • CV1586: Cross-modal knowledge distillation

    • MERL is seeking an intern to conduct research in the area of cross-modal knowledge distillation (RGB to IR, RGB to Lidar etc.) for applications in computer vision. The ideal candidate is a senior PhD student with experience in deep learning and computer vision and a good publication record at top-tier venues. Prior knowledge and experience with knowledge distillation and multiple modalities strongly preferred. 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. 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
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  • CV1546: Vibration analysis in video sequences

    • MERL is looking for a self-motivated intern to work on vibration analysis in video sequences. The ideal candidate would be a Ph.D. student with a strong background in machine learning, optimization and computer vision. Experience in computational photography and MATLAB/Python is a plus. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The internship is for a minimum of 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: Computer Vision, Machine Learning, Optimization
    • Host: Jeroen van Baar
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  • CV1567: Generative Adversarial Networks (GANs) for 3D face generation

    • MERL is seeking a highly motivated intern to conduct original research in the area of generative adversarial networks for realistic 3D face generation. 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 with experience in 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: Computer Vision, Machine Learning
    • Host: Tim Marks
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  • CV1534: 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/Tensorflow is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The internship is for 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: Computer Vision, Machine Learning
    • Host: Mike Jones
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  • CV1552: Multimodal Reasoning

    • 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 PhD student with a strong mathematical background in machine learning and computer vision. The candidate must have prior experience in using deep learning methods for image and video representations (such as using scene graphs) and deep audio analysis (such as source separation, localization, etc.). Proficiency in Python and flexibility in using different deep learning software (especially Pytorch) is expected. The intern is expected to collaborate with computer vision and speech teams at MERL to develop algorithms and prepare manuscripts for scientific publications. The internship is for 3 months with 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, Speech & Audio
    • Host: Anoop Cherian
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  • CV1545: Multi-modal Perception for Robotic Tool Manipulation

    • MERL is looking for a self-motivated intern to work on multi-modal perception for robotic tool manipulation. The intern will help to develop new ideas for improving the state of the art. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision. Experience in robotics, reinforcement learning and physics engines (MuJoCo) is desired. Proficiency in Python programming and Pytorch/Tensorflow is required. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The internship is for a minimum of 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: Computer Vision, Machine Learning, Robotics
    • Host: Jeroen van Baar
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  • CV1541: 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 current Ph.D. student with a strong background in computer vision, deep learning, and/or robotics. There are several available topics for consideration including learning for object manipulation, grasp detection and regrasping, pose estimation, and intent recognition for human-robot interaction. The internship requires development of novel algorithms which can be implemented and evaluated on a robotic test-bed. Experience in working with a physics engine simulator like Mujoco, pyBullet, or Gazebo is required. 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
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  • CV1540: 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, 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
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