Internship Openings

15 / 61 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).


  • SP1277: Vehicular Mobility Control Technologies

    • 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 mobility control. The candidate is expected to develop innovative edge computing technology to realize real time vehicle dynamics control. The candidates should have knowledge of the vehicular networking and control optimization. Knowledge of the Python programming and SUMO simulator is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.

    • Research Areas: Communications, Control, Optimization
    • Position ID: SP1277
    • Contact: Jianlin Guo
    • Email: guo[at]merl[dot]com
    • To be considered please send CV and Position ID to the contact email.
  • SP1267: Signal spectrum analysis

    • MERL is looking for a self-motivated intern to work on signal spectrum analysis. The ideal candidate would be a senior Ph.D. student with strong background in signal processing, compressive sensing, and mathematics. Proficiency in MATLAB and Python programming is necessary. Experience in analyzing real experimental data or detecting weak signal in noisy environment is a great asset. The intern is expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Start date is flexible.

    • Research Areas: Computational Sensing, Optimization, Signal Processing
    • Host: Dehong Liu
    • Apply Now
  • CV1284: Deep Learning in Computer Vision

    • MERL is looking for a self-motivated intern to work on deep learning in computer vision. The ideal candidate would be a Ph.D. student with a strong background in machine learning, optimization and computer vision. Proficiency in deep learning toolboxes such as PyTorch and Tensorflow 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, Optimization
    • Host: Ziming Zhang
    • Apply Now
  • CV1283: Understanding of Deep Learning

    • MERL is looking for a self-motivated intern to work on understanding of deep learning. The ideal candidate would be a Ph.D. student with a strong background in machine learning, optimization and computer vision. Proficiency in deep learning toolbox such as PyTorch and Tensorflow 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, Optimization
    • Host: Ziming Zhang
    • Apply Now
  • MP1248: Realtime Optimization and Control

    • MERL is seeking an intern to develop realtime optimization strategies to maximize the performance of vapor compression systems. The ideal candidate has a strong theoretical background in extremum seeking, nonlinear and adaptive control, and optimization. Proficiency in MATLAB, Python or Julia is required. Senior graduate students in engineering or mathematics programs at leading research universities are sought. The intern will collaborate with MERL researchers in developing new algorithms, conducting experiments, and preparing manuscripts for scientific publications. The expected duration is roughly three months.

    • Research Areas: Control, Multi-Physical Modeling, Optimization
    • Host: Dan Burns
    • Apply Now
  • MP1249: Control of Systems with Hybrid Dynamics

    • MERL is seeking an intern to develop optimal control strategies for mode-switching vapor compression systems characterized by discrete changes in dynamics. The ideal candidate has a strong background in hybrid systems, optimization, and event-based control. Proficiency in MATLAB, Python or Julia is required. Senior students enrolled in doctoral programs in engineering, applied mathematics or related fields are encouraged to apply. It is expected that the intern will assist in preparation of results for publication in scientific venues. The duration of the internship is approximately three months.

    • Research Areas: Control, Multi-Physical Modeling, Optimization
    • Host: Dan Burns
    • 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
  • CD1247: Control of Space Vehicles

    • MERL's Mechatronics group is seeking a highly motivated intern for a research position in control of space vehicles. The ideal candidate is working towards a Ph.D. in aerospace, mechanical, or electrical engineering, and has background in both optimization-based control and space vehicle dynamics. The candidate is expected to possess strong abilities in algorithm analysis and Matlab implementation. The duration of the internship is approximately 3 months. Publication of results produced during the internship is expected.

    • Research Areas: Control, Dynamical Systems, Optimization
    • Host: Avishai Weiss
    • Apply Now
  • CD1270: Numerical Optimization Algorithms for Predictive Control

    • MERL is looking for a highly motivated individual to work on efficient numerical algorithms and applications of optimization based control methods. The research will involve the study and development of novel optimization techniques for predictive control and estimation and/or the implementation and validation of algorithms for industrial applications. The ideal candidate should have experience in either one or multiple of the following topics: convex and non-convex optimization, Newton-type optimization algorithms, numerical optimization (e.g. active-set or interior point) and optimal control. PhD students in engineering or mathematics with a focus on numerical optimization or numerical optimal control are encouraged to apply. Publication of results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in Matlab is required; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is roughly 3 months and the start date is flexible.

    • Research Areas: Control, Dynamical Systems, 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
  • CD1298: Theoretical and computational aspects of mean-field control

    • We are looking for a graduate student intern to work on theoretical and computational aspects of mean-field control and mean-field games. An ideal candidate will be a graduate student working on MFC/MFG or Optimal Transport. Expertise in TWO or more of the following areas is required: 1). Optimal control 2). Control of PDEs 3). Geometric methods of dynamical systems theory 4). Statistical Mechanics 5). Stochastic analysis 6). Optimal Transport. Ph.D. students from top programs in engineering, physics, applied math are encouraged to apply. The duration of the internship will be 3-6 months. Publication of results is highly encouraged.

    • Research Areas: Applied Physics, Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Piyush Grover
    • Apply Now
  • CD1296: Optimization and Control of Thermo-fluid Systems

  • CD1295: Modeling and data-assimilation of atmospheric flows

  • DA1286: Optimization Algorithms

    • The Data Analytics group at MERL is seeking highly motivated intern to work on the development of novel optimization algorithms. The target applications span a broad range of areas including machine learning, scheduling, and transportation. Successful candidate will collaborate with MERL researchers to develop and implement new algorithms, conduct experiments, and prepare results for publication. Ideal candidate would be senior PhD student with experience in one or more of the following areas: machine learning, mathematical optimization. Strong programming skills and fluency in C++/Python are expected. Prior experience with popular optimization packages such as Ipopt, Gurobi, Cplex, PyTorch is a plus. The duration of the internship is expected to be 3 months. Start date is flexible.

    • Research Areas: Artificial Intelligence, Machine Learning, Optimization
    • Host: Arvind Raghunathan
    • Apply Now
  • AL1031: 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