Internship Openings

50 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, Sudan and Syria (Country Groups E:1 and E:2 of Part 740, Supplement 1, of the U.S. Export Administration Regulations).


  • CV1373: 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 is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Start date is flexible.

    • Research Areas: Computer Vision
    • Host: Mike Jones
    • Apply Now
  • CV1364: Graph neural networks for autonomous driving

    • MERL is looking for a self-motivated intern to work on graph neural networks. There are several available topics to choose from. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision. Proficiency in Python programming is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Start date is flexible.

    • Research Areas: Computer Vision, Machine Learning, Signal Processing
    • Host: Siheng Chen
    • Apply Now
  • CV1375: Computer Vision for Robotic Manipulation

    • MERL is looking for a highly motivated intern to work on computer vision for robotic manipulation. There are several available topics to choose from including active perception, grasp detection, and intent recognition for human-robot interaction. The ideal candidate would be a Ph.D. student with a strong background in computer vision, deep learning, and/or robotics. Proficiency in Python programming is necessary and experience with ROS is a plus. Successful candidate will collaborate with MERL researchers to develop algorithms, conduct experiments, and prepare manuscripts for scientific publications. Start date is flexible and expected duration of the internship is at least 3 months. Interested candidates are encouraged to apply with their recent CV, list of related publications, and/or links to GitHub repositories (if any).

    • Research Areas: Computer Vision, Machine Learning, Robotics
    • Host: Siddarth Jain
    • Apply Now
  • CV1372: 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/webly-supervised learning, 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 expected to be at least 3 months with a flexible start date.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Kuan-Chuan Peng
    • Apply Now
  • CV1376: Visual Reasoning

    • MERL is looking for a self-motivated intern to work on problems at the intersection of visual reasoning, representation learning, and language models. 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). Prior experience in using deep learning methods for visual reasoning tasks (such as scene-graph-based inference, compositional reasoning in VQA, image/video captioning, physics-based reasoning, etc) is a plus. Proficiency in Python and PyTorch is expected. The intern will collaborate with MERL researchers to invent novel machine learning models, conduct experiments, and publish in top-tier venues. The internship is for 3-6 months with flexible start date.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
    • Host: Anoop Cherian
    • Apply Now
  • CV1386: Machine Learning for Computer Vision

    • MERL is looking for a self-motivated intern to work on machine learning for computer vision. There are several available topics to choose from. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision. Publication in a top-tier machine learning or computer vision venue (NIPS, CVPR, ECCV, ICCV, ICML, PAMI, etc) is preferred. Proficiency in Python programming is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The internship would be 3-6 months, and the start date is flexible.

    • Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • Host: Jeroen van Baar
    • Apply Now
  • CV1365: Machine learning for 3D computer vision

    • MERL is looking for a self-motivated intern to work on machine learning for 3D computer vision. There are several available topics to choose from. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision. Proficiency in Python programming is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Start date is flexible.

    • Research Areas: Computer Vision, Machine Learning
    • Host: Siheng Chen
    • Apply Now
  • CV1374: Efficient Deep Networks

    • MERL is looking for a self-motivated intern to work on the problem of learning deep networks that are both memory and time efficient. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision and some experience with techniques related to model compression, knowledge distillation, etc. Proficiency in Python programming and TensorFlow or PyTorch is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Start date is flexible.

    • Research Areas: Computer Vision, Machine Learning
    • Host: Mike Jones
    • Apply Now
  • CD1383: Collaborative Estimation for Robotic Manipulators

    • MERL is seeking a highly skilled and self-motivated intern to conduct research on condition monitoring for robotic manipulators. The ideal candidate should have solid backgrounds in robotic manipulators, stochastic estimation methods for dynamical systems, and collaborative strategies over multi-agents. Experience of applying machine learning to dynamical systems is a strong plus. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, robotics, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 3 months.

    • Research Areas: Dynamical Systems, Machine Learning, Robotics
    • Host: Yebin Wang
    • Apply Now
  • CD1377: 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
  • CD1393: Estimation and Learning of Decentralized Systems

    • The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to conduct research on estimation and learning of decentralized systems. The ideal candidate is a PhD candidate in electrical engineering, statistics, or related fields, and has expertise in some of Kalman filters, particle filters, multiple model filters, Bayesian learning, centralized/decentralized sensor fusion, and optimization-based estimation. Previous experience in any of ground vehicle estimation and satellite navigation systems is a merit. Publication of relevant results in conference proceedings and journals is expected, and so is capability of implementing algorithms in Matlab. The expected duration of the internship is 3-6 months with flexible start date.

    • Research Areas: Control, Machine Learning, Signal Processing
    • Host: Karl Berntorp
    • Apply Now
  • CD1382: Motion Planning in Dynamic Environment

    • MERL is seeking a highly skilled and self-motivated intern to work on motion planning of nonholonomic system in dynamic environments. The ideal candidate should have solid backgrounds in task allocation, scheduling, and motion planning under dynamic and stochastic environment. Excellent coding skill and strong publication records are necessary. Senior Ph.D. students in control, computer science, or related areas are encouraged to apply. Start date for this internship is flexible, and the expected duration is about 3 months.

    • Research Areas: Control, Optimization, Robotics
    • Host: Yebin Wang
    • Apply Now
  • CD1400: Autonomous Vehicle Planning and Control

    • The Control and Dynamical Systems (CD) group at MERL is seeking highly motivated interns at different levels of expertise to conduct 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. PhD students will be considered for algorithm development and analysis, and property proving. Master students will be considered for development and implementation in a scaled robotic test bench for autonomous vehicles. For algorithm development and analysis it is highly desirable to have deep background in one or more among: sampling-based planning methods, particle filtering, model predictive control, reachability methods, formal methods and abstractions of dynamical systems, and experience with their implementation in Matlab/Python/C++. For algorithm implementation, it is required to have working knowledge of Matlab, C++, and ROS, and it is a plus to have background in some of the above mentioned methods. The expected duration of the internship is 3-6 months with flexible start date after April 1st, 2020.

    • Research Areas: Artificial Intelligence, Control, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CD1391: Algorithms for GNSS localization

    • The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to work on GNSS Positioning and localization. Previous experience with at least some of the GNSSs, particle filtering, interacting multiple model filtering, Kalman-type filtering, and ambiguity resolution is highly desirable. Working knowledge of C/C++ is required, and previous experience with GNSS packages such as RTKLib is highly desired. PhD candidates meeting the above requirements are welcome to apply. The expected duration of the internship is 3-6 months with flexible start date.

    • Research Areas: Control, Signal Processing
    • Host: Karl Berntorp
    • Apply Now
  • CD1399: Optimization Algorithms for Stochastic Predictive Control

    • MERL is looking for a highly motivated individual to work on tailored numerical optimization algorithms and applications of stochastic learning-based model predictive control (MPC) methods. The research will involve the study and development of novel optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: stochastic MPC (e.g., scenario trees or tube MPC), convex and non-convex optimization, machine learning, numerical optimization and (inverse) optimal control. PhD students in engineering or mathematics with a focus on stochastic (learning-based) MPC 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 is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months and the start date is flexible.

    • Research Areas: Control, Machine Learning, Optimization
    • Host: Rien Quirynen
    • Apply Now
  • CD1300: 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: Bram Goldsmith
    • Apply Now
  • CD1388: Mixed-Integer Optimal Control Algorithms

    • MERL is looking for highly motivated individuals to work on efficient numerical algorithms and applications of mixed-integer optimal control methods. The research will involve some among the following: the study and development of mixed-integer optimization techniques for optimal control, the implementation and validation of algorithms for relevant control applications. The ideal candidate should have experience in branch-and-bound methods and presolve techniques for mixed-integer optimization and/or model predictive control. PhD students in engineering or mathematics with a focus on mixed-integer optimization or numerical optimal control are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in Matlab is expected; coding parts of the algorithms in C/C++ is a big plus. The expected duration of the internship is 3-6 months and the start date is flexible.

    • Research Areas: Control, Dynamical Systems, Optimization
    • Host: Rien Quirynen
    • Apply Now
  • CD1140: MPC-based modular control architectures

    • MERL's Mechatronics group is seeking a highly motivated intern for performing research in Optimization based modular control architectures. The ideal candidate is working towards a Ph.D. in electrical, mechanical, or aerospace engineering, or in computer science, and has background in model predictive control, optimization, set-based methods, and modular control architectures. The candidate is expected to possess strong abilities in theorem proving, and algorithm development, analysis, and Matlab implementation. The internship start date is flexible and the duration is approximately 3 months, with possible extension. Publication of the results produced during the internship is expected.

    • Research Areas: Control
    • Host: Stefano Di Cairano
    • Apply Now
  • CD1260: Model Predictive Control of Hybrid Systems

    • The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to work on hybrid model predictive control. The scope of work includes the development of model predictive control algorithms for hybrid dynamical systems, switched systems, and quantized systems, analysis and property proving, and applications in automotive, space systems, and energy systems. PhD students with expertise in some among control, optimization, model predictive control and hybrid systems, and with working knowledge of Matlab implementation are welcome to apply. The expected duration of the internship is 3-6 months with flexible start date.

    • Research Areas: Control, Dynamical Systems, Optimization
    • Host: Stefano Di Cairano
    • Apply Now
  • CD1402: Predictive control for Performance and Perception Optimization

    • The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to conduct research on stochastic planning and control for concurrently achieving control performance while concurrently improving the perception of the environment. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Computer Science or related program, with focus on Control Theory. The ideal candidate will have experience in (one or more of) predictive control or model-based motion planning, stochastic control and estimation, interaction between control and estimation algorithms, control with chance constraints, tube-based control. Good programming skills in Matlab (or alternatively Python) are required, working knowledge of C/C++ is a plus. The expected duration of the internship is 3-6 months with flexible start date after April 1st, 2020.

    • Research Areas: Control, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CD1401: Formal Synthesis for Planning and Control for Autonomous Systems

    • The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to conduct research on planning and control by formal methods, in particular temporal logics specifications and their synthesis by mixed-integer inequalities. The ideal candidate is enrolled in a PhD program in Electrical, Mechanical, Aerospace Engineering, Computer Science or related program, with focus on Control Theory. The ideal candidate will have experience in (one or more of) formal methods, particularly temporal logics and signal temporal logics, reachability analysis, abstractions of dynamical systems, hybrid predictive control, and mixed integer programming. Good programming skills in Matlab (or alternatively Python) are required, working knowledge of C/C++ is a plus. The expected duration of the internship is 3-6 months with flexible start date after April 1st, 2020.

    • Research Areas: Control, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CD1392: Statistical Estimation, Learning, and Control of Dynamical Systems

    • The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to conduct fundamental research on statistical estimation and control. The scope of the internship includes development of algorithms and property proving for estimation and control of stochastic dynamical systems. PhD students with expertise in several of sequential Monte Carlo methods, Gaussian processes, Gaussian-process state-space models, model predictive control, are welcome to apply. The candidate is expected to be proficient in Matlab, and publication of the results produced during the internship is expected. The internship duration is 3 months with flexible start date.

    • Research Areas: Control, Machine Learning, Optimization
    • Host: Karl Berntorp
    • Apply Now
  • CD1405: Mechanism design for mobility

    • Mobility; externalities; mechanism design. We are looking for a talented and driven individual to help us design an efficient and equitable mobility solution. This position requires a deep understanding of mechanism design and at least some programming ability. Preference will be given to candidates with a background in transportation.

    • Research Areas: Optimization
    • Host: Uroš Kalabić
    • Apply Now
  • MP1397: High frequency high power FinFET Device Technology

    • MERL is seeking a highly self-motivated and qualified intern to work and perform cutting-edge research in the area of Advanced RF Power Devices. The candidate is expected to have a strong technical background in Applied Physics, Semiconductor Physics and Computational Physics. Candidate is expected to carry out 3D simulation using a TCAD simulator and Matlab software. The ideal candidate should be in his/her senior year of a Ph.D. program in Electrical Engineering and/or Physics. He/she is expected to explore different 3D device architectures and using first principle physics to explain important physical mechanisms and phenomena. Candidate with knowledge in FinFET device design is desirable. The duration of the internship is expected to be 3 months with flexible starting date.

    • Research Areas: Electronic and Photonic Devices
    • Host: Koon Hoo Teo
    • Apply Now
  • MP1384: 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 for process applications. 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.

    • Research Areas: Multi-Physical Modeling
    • Host: Chris Laughman
    • Apply Now
  • MP1381: Electric Motor Design

    • MERL is seeking a motivated and qualified individual to conduct research in design, modeling, and simulation of electrical machines. The ideal candidate should have solid backgrounds in modeling (including model reduction)/co-simulation of electromagnetics and thermal dynamics of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and real-time control experiments involving motor drives is a strong plus. Senior Ph.D. students in electrical or mechanical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3-6 months.

    • Research Areas: Applied Physics, Electric Systems, Multi-Physical Modeling
    • Host: Bingnan Wang
    • Apply Now
  • MP1263: Fault analysis for electric motors

    • MERL is seeking a highly motivated intern to conduct research in electric machine fault analysis. The ideal candidate should be a senior Ph. D student in Electrical Engineering or related discipline with a solid background in the physics and engineering of electric motors, and early fault detection. Knowledge and experience in electric motor modeling and machine learning are desired. The candidate is expected to collaborate with MERL researchers to conduct theoretical analysis, numerical simulations, develop algorithms and prepare manuscripts for scientific publications. The duration of internship is expected to be 3 months and start date is flexible.

    • Research Areas: Applied Physics, Electric Systems, Multi-Physical Modeling
    • Host: Bingnan Wang
    • Apply Now
  • MP1406: Numerical Analysis of Electric Machines

    • MERL is seeking a motivated and qualified intern to conduct research in the design, modeling and optimization of electrical machines. The ideal candidate should have solid backgrounds in electromagnetic theory, electric machine design, and numerical modeling techniques (including model reduction), research experiences in electric, magnetic, and thermal modeling and analysis of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and optimization techniques is a strong plus. Senior Ph.D. students in electrical or mechanical engineering with related expertise are encouraged to apply. Start date for this internship is flexible and the duration is 3-6 months.

    • Research Areas: Dynamical Systems, Multi-Physical Modeling, Optimization
    • Host: Bingnan Wang
    • Apply Now
  • SA1358: Multimodal AI

    • MERL is looking for an intern to work on fundamental research in the area of audiovisual semantic understanding for scene-aware dialog technologies by combining end-to-end dialog and video scene understanding technologies. The intern will collaborate with MERL researchers to derive and implement new models, conduct experiments, and prepare results for high impact publication. The ideal candidate would be a senior Ph.D. student with experience in one or more of video captioning/description, end-to-end conversation modeling and natural language processing including practical machine learning algorithms with related programming skills. The duration of the internship is expected to be 3-6 months.

    • Research Areas: Speech & Audio
    • Host: Chiori Hori
    • Apply Now
  • SA1031: Distributed auctions for network welfare maximization

    • We are looking for a talented individual to collaborate and facilitate research on new algorithms in mechanism design and distributed auctions. Responsibilities will include mathematical modeling, algorithm design, software prototyping, and running Monte Carlo simulations in a network traffic domain. Candidates should be strong scientific programmers and have some background in numerical optimization, simulation design, and auction theory.

    • Research Areas: Optimization
    • Host: Matt Brand
    • Apply Now
  • SA1359: End-to-end speech and audio analysis recognition and understanding

    • MERL is looking for interns to work on fundamental research in the area of end-to-end speech and audio analysis, recognition, and understanding using machine learning techniques such as deep learning. The intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for high impact publication. The ideal candidates would be senior Ph.D. students with experience in one or more of source separation, speech recognition, and natural language processing including practical machine learning algorithms with related programming skills. The duration of the internship is expected to be 3-6 months. Positions are available immediately and throughout 2020.

    • Research Areas: Speech & Audio
    • Host: Takaaki Hori
    • Apply Now
  • SA1320: Deep Network Information Security

    • We are seeking graduate students interested in helping advance the field of network and information security using the latest developments in deep learning. 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 networks, natural language processing, and network security. The duration of the internship is expected to be 3-6 months. The internship can be scheduled either during the summer or at another time of year.

    • Research Areas: Machine Learning, Speech & Audio
    • Host: Bret Harsham
    • Apply Now
  • SA1357: Audio Analysis and Source Separation

    • We are seeking multiple graduate students interested in helping advance the field of source separation, speech enhancement, and sound event detection in extreme environments using the latest developments in deep learning. 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 candidates would be senior Ph.D. students with experience in audio signal processing, speech modeling, probabilistic modeling, and deep learning. The duration of the internship is expected to be 3-6 months. Positions are available immediately and throughout 2020.

    • Research Areas: Machine Learning, Speech & Audio
    • Host: Gordon Wichern
    • Apply Now
  • SP1366: 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.

    • Research Areas: Artificial Intelligence, Machine Learning, Signal Processing
    • Host: Ye Wang
    • Apply Now
  • SP1410: Coherent Optical Sensing

    • MERL is seeking an intern to work on coherent optical sensing. The ideal candidate would be an experienced PhD student or post-graduate researcher working in coherent sensing. The candidate should have a detailed knowledge of optical interferometry and imaging with a focus on either optical coherence tomography, optical coherence microscopy or FMCW LIDAR. Strong programming skills in Matlab are essential. Experience of working in an optical lab environment would be advantageous. Duration is 3 to 6 months.

    • Research Areas: Computational Sensing, Signal Processing
    • Host: David Millar
    • Apply Now
  • SP1370: Machine Learning based DPD for Power Amplifier

    • 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.

    • Research Areas: Communications, Electronic and Photonic Devices, Machine Learning, Signal Processing
    • Host: Rui Ma
    • Apply Now
  • SP1404: Photonic device design 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 and MODE), design, and testing of active and passive devices for optical communications, as well as hands-on experience in deep learning (such as autoencoders and GAN using Python and Tensorflow/keras/pytorch). Experience in silicon photonics, photonic crystal, plasmonicss, optimization algorithms, machine learning, 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: Artificial Intelligence, Electronic and Photonic Devices
    • Host: Keisuke Kojima
    • Apply Now
  • SP1368: AI-enhanced security

    • MERL is seeking a highly motivated and qualified intern to work on AI-enhanced security. The candidate is expected to develop innovative AI technologies for cybersecurity applications. Candidates should have strong knowledge and hands on experience in the areas of neural network and learning techniques, such as feature extraction, machine learning, deep learning, shallow learning, and distributed learning. Proficient programming skills with Python, Matlab, and C++, and strong mathematical analysis will be required to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Artificial Intelligence, Machine Learning, Signal Processing
    • Host: K.J. Kim
    • Apply Now
  • SP1155: Coexistence of the Heterogeneous Wireless Technologies

    • MERL is seeking a highly motivated, qualified intern to join the Electronics and Communications group for a three month internship program. The ideal candidate will be expected to carry out research on coexistence of the heterogeneous wireless technologies in the Sub-1 GHz (S1G) band. The candidate is expected to develop innovative coexistence technology for IEEE 802.15.4g to mitigate interference caused by other S1G technologies such as IEEE 802.11ah, LoRa and SigFox. The candidates should have knowledge of 802.15.4g and 802.11ah protocols. Additionally, the candidate should also be familiar with NS3 simulators. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Communications, Signal Processing
    • Host: Jianlin Guo
    • Apply Now
  • SP1307: Vehicular traffic environment sensing

    • MERL is seeking a highly motivated, qualified intern to join a three month internship program. The ideal candidate will be expected to carry out research on environmental sensing in high frequency bands. The candidate is expected to develop innovative sensing technologies. Candidates should have strong knowledge about neural network and learning techniques, such as machine learning, deep learning, shallow learning, and distributed learning. In addition, understanding of spectrum sensing and wireless communications technologies is necessary. Proficient programming skills with Python, 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.

    • Research Areas: Signal Processing
    • Host: K.J. Kim
    • Apply Now
  • SP1379: Intelligent Brain-Machine Interface

    • The Signal Processing group at MERL is seeking a highly motivated, qualified individual to join our 3-month internship program of research on stressless man-machine interface with multi-modal bio-sensors. The ideal candidate is expected to possess an excellent background in brain-machine interfaces, data analytics, machine learning, sensor design, electro-magnetic analysis, augmented reality, and sensing algorithms. Candidates who hold a PhD or in their senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Signal Processing
    • Host: Toshi Koike-Akino
    • Apply Now
  • SP1409: Coherent optical transmission systems

    • MERL is seeking an intern to work on systems and subsystems for coherent optical fiber transmission. The ideal candidate would be an experienced PhD student or post-graduate researcher working in optical communications. The candidate should have a detailed knowledge of optical communications systems at the physical layer and digital signal processing for digital coherent communication, with a focus on optical fiber communication. Strong programming skills in Matlab are essential. Experience of working in a lab environment would be advantageous. Duration is 3 to 6 months.

    • Research Areas: Communications, Signal Processing
    • Host: Kieran Parsons
    • Apply Now
  • SP1371: Object Tracking and Perception for Autonomous Driving

    • The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in automotive radar-based object tracking and perception for autonomous driving. Previous experience on multiple (point and extended) object tracking, data association, and data-driven object detection/tracking is highly preferred. Knowledge about automotive radar schemes (MIMO array and waveform modulation (FMCW, PMCW, and OFDM)) and hands-on experience on open automotive datasets are a plus. Knowledge on vehicle dynamics is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, conduct field measurements, data analysis (Python & MATLAB), 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, Computational Sensing, Dynamical Systems, Machine Learning, Signal Processing
    • Host: Perry Wang
    • Apply Now
  • SP1398: Electrical machine modeling

    • MERL is looking for a self-motivated intern to work on electrical machine modelling and signal processing. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in electrical machines, signal processing, and electrical circuit analysis. Experience in transient analysis of electrical machines is desirable. Proficiency in MATLAB and simulink is necessary. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. The total duration is 3 months.

    • Research Areas: Computational Sensing, Electric Systems, Signal Processing
    • Host: Dehong Liu
    • Apply Now
  • SP1369: Advanced Vehicular Communications

    • MERL is seeking a highly motivated, 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 the vehicular communications and networking. The candidate is expected to develop innovative technology to achieve reliable and low latency V2X communications in the vehicular networks. The candidates should have knowledge of the vehicular communication technology such as IEEE 802.11p or 3GPP C-V2X. Knowledge of the vehicular control is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Communications, Control, Machine Learning
    • Host: Jianlin Guo
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  • DA1396: Machine learning for Contact-rich Robotic Manipulation

    • MERL is looking for a highly motivated individual to work on contact-rich robotic manipulation applications. The ideal candidate is expected to have expertise both in machine learning techniques, such as Gaussian Process Regression and Deep Neural Networks, as well as in reinforcement learning algorithms. In addition, having experience with robotic systems would be considered a significant plus. The candidate will be expected to develop novel algorithms and possibly implement them on robotic systems. Proficiency in Python programming is necessary, and experience with ROS would be a plus. The candidate will collaborate closely with MERL researchers. Start date for this internship is flexible, and the duration is expected to be 3-6 months.

    • Research Areas: Artificial Intelligence, Data Analytics, Machine Learning, Robotics
    • Host: Diego Romeres
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  • DA1380: Machine Learning and Optimization

    • MERL is looking for a self-motivated intern to develop predictive machine learning and optimization algorithms. Applications include time series prediction, anomaly detection, scheduling, and transportation. The ideal candidate would be a senior PhD student with experience in one or more of the following areas: machine learning, mathematical optimization, discrete-event systems modeling. Strong programming skills using C++/Python are expected. Experience with libraries such as scikit-learn, Pytorch is 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.

    • Research Areas: Data Analytics, Machine Learning
    • Host: BinBin Zhang
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  • DA1387: Disaster-resilient Power Grid

    • MERL is seeking a highly motivated and qualified individual to join our internship program and conduct the research in the area of disaster-resilient power grid. The ideal candidate should have solid background in power systems, mathematical optimization, stochastic analysis, 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.

    • Research Areas: Electric Systems
    • Host: Hongbo Sun
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  • DA1389: Time Series Algorithms using Generative Models or Reinforcement Learning

    • The Data Analytics Group is looking for a self-motivated intern to develop algorithms with applications in industrial time series data. The ideal candidate is a senior PhD student with one of the following profiles: 1) a candidate with experience in deep generative models (VAE, GAN, Boltzmann Machines, etc.), classical generative models, or in applying modern methods of machine learning to time series data; 2) a candidate with experience in reinforcement learning algorithms using time series data. Preferred candidates will have a background working with data outside computer vision. The candidate should have strong programming skills using Python and/or C++. The outcome of a successful internship will be an intern-driven algorithm development that leads to a scientific publication. Typical internship length is 3 months with a flexible start date.

    • Research Areas: Data Analytics
    • Host: Emil Laftchiev
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  • DA1367: Safe reinforcement and deep learning algorithms

    • MERL is seeking a motivated and qualified individual to conduct research in safe reinforcement learning (RL) and deep learning algorithms for robotics applications. The ideal candidate should have solid background in RL. Knowledge of deep learning algorithms is a plus. Publication of the results produced during the internship is anticipated. Duration of the internship is expected to be 3 months. Start date is flexible.

    • Research Areas: Artificial Intelligence, Control, Robotics
    • Host: Mouhacine Benosman
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