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.

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.

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.

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.

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.

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.

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.

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.

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.

MERL is looking for a self-motivated and qualified candidate to work on Bayesian Optimization for industrial applications. The ideal candidate is a PhD student with experience and peer-reviewed publications in the general field of derivative-free/zeroth-order optimization, preference will be given to candidates who have contributed to theoretical advances or practical application of Bayesian optimization, especially for multi-objective optimization problems. The ideal candidate will have a strong general understanding of numerical optimization and probabilistic machine learning e.g. Gaussian process regression, and is expected to develop, in collaboration with MERL researchers, state of the art algorithms to optimize parameters for industrial processes or control systems. An expected outcome of the internship is one or more peer-reviewed publications. Typical internship length is 3-4 months.

MERL is looking for a highly motivated individual to work on hierarchical reinforcement learning for robotic applications. The research will develop novel algorithms for hierarchical reinforcement learning and evaluate them on challenging long horizon robotic problems. The ideal candidate must have experience in either one or multiple of the following topics: (Deep) Reinforcement learning, Hierarchical RL, policy optimization and Markov Decision Processes (MDPs). Senior PhD students in machine learning and engineering with a focus on Reinforcement Learning are encouraged to apply. Prior experience working with physics engines like Mujoco, Bullet, etc. is required. A successful internship will result in submission of results to peer-reviewed conference and journals. Good coding skills in Python and state-of-the-art RL environments (e.g., RL Bench) is required. The expected duration of internship is 3-4 months and the start date is flexible. This internship is preferred to be onsite at MERL.

MERL is seeking a highly motivated and qualified intern to jconduct research on advanced signal processing for RF-controlled metasurfaces. The candidate is expected to develop innovative integrated sensing and communications techniques for RF-controlled metasurface-aided various applications. Candidates should have strong knowledge about positioning/localization, tracking, passive beamforming, interference mitigation, and optimization. Proficient programming skills with Python and MATLAB, and strong mathematical analysis will be additional assets to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. The expected duration of the internship is 3 months, with a flexible start date.

The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in automotive radar perception. Expertise in deep learning-based object detection, multiple object tracking, data association, and representation learning (detection points, heatmaps, and raw radar waveforms) is required. Previous hands-on experience on open automotive datasets is a plus. Familiarity with the concept of FMCW, MIMO, and range-Doppler-angle spectrum is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date.

The Computational Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in integrated sensing and communication (ISAC) with a focus on signal processing for WLAN sensing. Expertise in waveform/sequence optimization, integrated precoding for ISAC, and ToF-Doppler-Angle spectrum estimation using Wi-Fi packets is highly desired. Familiarity with IEEE 802.11 (ac/ax/ad/ay) standards is a plus. Knowledge of Wi-Fi-based localization, occupancy sensing, device-free pose/gesture recognition, skeleton tracking, and multi-modal fusion is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments using MERL in-house testbed, and prepare results for publication. The expected duration of the internship is 3 months with a flexible start date.

The Signal Processing (SP) group at MERL is seeking a highly motivated intern to conduct fundamental research in THz (Terahertz) sensing. Expertise in statistical inference, unsupervised anomaly detection, and deep learning (spatial-temporal representation learning) is required. Previous hands-on experience in THz data analysis is a plus. Familiarity with python and deep learning libraries is a must. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with collaborators, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date.

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.

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.

MERL is seeking a motivated and qualified intern to conduct research on topology optimization of electrical machines. The ideal candidate should have solid background and demonstrated research experience in mathematical optimization methods, in particular in topology optimization, robust optimization, and sensitivity analysis. Hands-on coding experiences with the implementation of topology optimization algorithms and finite-element simulation are 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. The start date is flexible and typical duration is about 3 months.

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.

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.

MERL is looking for a highly motivated and qualified candidate to work on modeling for smart sustainable buildings. The ideal candidate will have a strong understanding of modeling renewable energy sources, grid-interactive buildings, occupant behavior, and dynamical systems with expertise demonstrated via, e.g., peer-reviewed publications. Hands-on programming experience with Modelica is preferred. The minimum duration of the internship is 12 weeks; start time is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

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.