Internship Openings

29 / 70 Intern positions were found.

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

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

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

Rising to the challenges of COVID-19

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

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


  • CA1933: Spacecraft Attitude Control

    • MERL is seeking a highly motivated intern for a research position in spacecraft attitude dynamics and control. The ideal candidate is a PhD student with experience in attitude kinematics and dynamics, multi-body dynamics, Lagrangian or Hamiltonian mechanics, optimization, and control of rigid bodies. Experience in computational fluid dynamics (CFD) using OpenFOAM, multi-phase flow modeling, and volume-of-fluid approach is desirable. Publication of results produced during the internship is expected. The duration of the internship is 3-6 months, and the start date is flexible.

    • Research Areas: Applied Physics, Control, Dynamical Systems, Multi-Physical Modeling
    • Host: Avishai Weiss
    • Apply Now
  • CA1905: Coordination and Control of Connected Autonomous Vehicles

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

    • Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Rien Quirynen
    • Apply Now
  • CA1940: Autonomous vehicle planning and contro in uncertain environments

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control for autonomous vehicles in uncertain surrounding environments. The research domain includes algorithms for path planning and control in environments that are uncertain and perceived by sensing and predicted according to models and data. The ideal candidate is expected to be working towards a PhD with strong emphasis in vehicle guidance and control, and to have interest and background in as many as possible of: vehicle dynamics modeling and control, sensor uncertainty modeling, data-driven prediction, predictive control for uncertain systems, motion planning. Good programming skills in MATLAB, Python are required, knowledge of C/C++, rapid prototyping systems, automatic code generation, vehicle simulation packages (CarSim, CarMaker) or ROS are a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.

    • Research Areas: Control, Dynamical Systems, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1954: Control and Motion Planning for Quadrotors

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

    • Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
    • Host: Marcus Greiff
    • Apply Now
  • CA1942: Model predictive control for system with perception uncertainty

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on design and analysis of model predictive control algorithms for systems subject to environment uncertainty that can be reduced by perception. The research domain includes algorithms for stabilizing uncertain and stochastic model predictive control, uncertainty quantification and reduction via estimation, optimization algorithms for uncertain and stochastic predictive control. The ideal candidate is expected to be working towards a PhD with strong emphasis in some of: stochastic model predictive control, statistical estimation, uncertainty quantification, and sensing-driven control. Good programming skills in MATLAB, Python are required, knowledge of C/C++ or ROS are a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.

    • Research Areas: Control, Dynamical Systems, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1941: Risk-aware fault tolerant control of autonomous vehicles

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on risk-aware, fault-tolerant planning and control of autonomous vehicles. The ideal candidate is expected to be working towards a PhD with strong emphasis in control or planning algorithms, and to have interest and background in as many as possible of: physics-based and data-based prediction models, formal methods for probabilistic validation, invariance and set-based control, predictive control algorithms for linear and nonlinear systems and vehicle modeling and control. Good programming skills in MATLAB, Python are required. Knowledge of ROS and C/C++ are a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.

    • Research Areas: Control, Dynamical Systems, Optimization, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1888: Perception-Aware Control

    • MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control algorithms accounting for perception and sensing uncertainty, e.g., the surrounding environment of a vehicle. The ideal candidate is expected to be working towards a PhD with strong emphasis in control or planning algorithms, and to have interest and background in as many as possible of: predictive control algorithms for linear and nonlinear systems, stochastic constrained control, e.g., chance constraints, stochastic optimization, statistical estimation, perception system modeling, Bayesian inference, and vehicle modeling and control. Good programming skills in MATLAB, Python or C/C++ are required. The expected start of of the internship is in early 2023, for a duration of 3-6 months.

    • Research Areas: Control, Optimization, Signal Processing
    • Host: Karl Berntorp
    • Apply Now
  • CA1706: Perception-aware vehicle control

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

    • Research Areas: Control, Optimization, Signal Processing
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1707: Autonomous vehicles guidance and control

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

    • Research Areas: Control, Dynamical Systems, Optimization
    • Host: Stefano Di Cairano
    • Apply Now
  • CA1904: Numerical Optimal Control for Hybrid Dynamical Systems

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

    • Research Areas: Control, Machine Learning, Optimization, Robotics
    • Host: Rien Quirynen
    • Apply Now
  • CA1929: Driver Classification for Future Mobility

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

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

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

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

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

    • Research Areas: Artificial Intelligence, Control, Machine Learning, Optimization, Robotics
    • Host: Abraham Vinod
    • Apply Now
  • CA1923: Locomotion of Legged Robots

    • MERL is looking for an intern to conduct research on locomotion of legged robots. The research spans multiple areas from modeling, motion planning, sensing and learning from data, to control. The ideal candidate will have experience in either one or multiple of the following topics: model predictive control, statistical estimation, machine learning, numerical optimization, control theory, and reinforcement learning. Good programming skills in MATLAB, ROS, Python, or C++ are required. Graduate students in robotics, engineering, or mathematics with a focus on legged robots, control theory, or numerical optimization are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. The expected duration of the internship is 3-6 months. The start date is flexible.

    • Research Areas: Control, Optimization, Robotics
    • Host: Marcel Menner
    • Apply Now
  • CA1932: Spacecraft Guidance, Navigation, and Control

    • MERL is seeking highly motivated interns for research positions in guidance, navigation, and control of spacecraft. The ideal candidates are PhD students with experience in one or more of the following topics: astrodynamics, the three-body problem, relative motion dynamics, rendezvous, landing, attitude control, orbit control, orbit determination, nonlinear estimation, and optimization-based control. Publication of results produced during the internship is expected. The duration of the internships are 3-6 months, and the start dates are flexible.

    • Research Areas: Control, Dynamical Systems, Optimization
    • Host: Avishai Weiss
    • Apply Now
  • MD1918: Robust/safe learning for motion planning and control

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

    • Research Areas: Control, Dynamical Systems, Machine Learning, Optimization
    • Host: Yebin Wang
    • Apply Now
  • MD1886: Co-design of robotic arm and control systems

    • MERL is seeking a highly motivated and qualified individual to conduct research in model-based robotic system design. The ideal candidate should have solid backgrounds in robotic dynamics and simulation, motion planning and control, simulation-based optimization, surrogate modeling, and coding skills. Demonstrated experience on implementing robotic dynamics and simulation/optimization software such as Matlab is a necessity. Ph.D. students in mechanical engineering, robotics, computer science, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

    • Research Areas: Control, Dynamical Systems, Optimization, Robotics
    • Host: Yebin Wang
    • Apply Now
  • MD1917: Learning and Optimization for Motor Drives

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

    • Research Areas: Control, Electric Systems, Machine Learning, Optimization
    • Host: Anantaram Varatharajan
    • Apply Now
  • MD1887: Optimization and control of xEV and electric aircraft

    • MERL is seeking a motivated and qualified individual to conduct research in modeling, control, simulation and analysis of electric system involved in xEV and electric aircraft. The ideal candidate should have solid backgrounds in some of the following areas: modeling, control, and simulation of electrical systems (including generators, motors, power electronics and batteries), aerodynamics, mission analysis, flight dynamics, and multi-disciplinary system design optimization. Demonstrated experience in software/language such as Modelica or Matlab/Simulink/Simscape is a necessity. Knowledge and experience of CarSim, NPSS, SUAVE, and FLOPS is a definite plus. Senior Ph.D. students in automotive, aerospace, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

    • Research Areas: Control, Electric Systems, Multi-Physical Modeling, Optimization
    • Host: Yebin Wang
    • Apply Now
  • CI1952: IoT Network Configuration Design

    • MERL is seeking a highly motivated and qualified intern to carry out research on communications network design methodology. The candidate is expected to develop innovative network technologies to support dynamic IoT networks with heterogenous nodes. The candidates should have knowledge of network technologies such as routing and path planning and network simulation tools such as ns3. Knowledge of communication standards such as IEEE 802 and 3GPP is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.

    • Research Areas: Communications, Control, Signal Processing
    • Host: Jianlin Guo
    • Apply Now
  • DA1926: Robotic Manipulation Control using VisuoTactile Sensing

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

    • Research Areas: Artificial Intelligence, Control, Machine Learning, Optimization, Robotics
    • Host: Devesh Jha
    • Apply Now
  • DA1895: Human Robot Interaction

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

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

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

    • Research Areas: Control, Dynamical Systems, Machine Learning
    • Host: Saleh Nabi
    • Apply Now
  • DA1931: Optimization Algorithms

    • 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 power systems, control, 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: linear and quadratic programming, active-set methods, and first-order methods for convex programs. Strong programming skills and fluency in C++/Python are expected. Prior experience with popular optimization packages such as Ipopt, Gurobi, Cplex is a plus. The duration of the internship is expected to be 3 months. Start date is flexible. 

    • Research Areas: Control, Optimization, Robotics
    • Host: Arvind Raghunathan
    • Apply Now
  • DA1935: Robot Learning Algorithms

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

    • Research Areas: Artificial Intelligence, Computer Vision, Control, Machine Learning, Robotics
    • Host: Daniel Nikovski
    • Apply Now
  • MS1957: Estimation and Model Structure Identification for Digital Twins

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

    • Research Areas: Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization
    • Host: Vedang Deshpande
    • Apply Now
  • MS1903: Bayesian Optimization and MPC for Net-Zero Energy Buildings

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

    • Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization
    • Host: Ankush Chakrabarty
    • Apply Now
  • MS1924: Data-driven Dynamic Modeling of Thermo-fluid systems

    • MERL is seeking a highly motivated and qualified individual to conduct research in dynamic modeling and simulation of vapor compression systems in the summer of 2023. Knowledge of data-driven modeling techniques is required. Experience in working with thermo-fluid systems is preferred. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. Senior Ph.D. students in applied mathematics, chemical/mechanical engineering and other related areas are encouraged to apply. The expected duration of the internship is 3 months and the start date is flexible.

    • Research Areas: Control, Multi-Physical Modeling
    • Host: Hongtao Qiao
    • Apply Now
  • MS1958: Simulation, Control, and Optimization of Large-Scale Systems

    • MERL is seeking a motivated graduate student to research numerical methods pertaining to the simulation, control, and optimization of large-scale systems. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in numerical methods, control, 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: Control, Multi-Physical Modeling, Optimization
    • Host: Chris Laughman
    • Apply Now