MERL is seeking a talented researcher to collaborate in the development of design algorithms for metalenses that are freeform, multilayer, and broadband. The ideal applicant will have a strong background in the relevant physics & maths, and has some fluency with the topology optimization and EM simulation tools commonly used in metasurface optics. Also desirable: familiarity with machine learning / AI tools and methods.

MERL is seeking a motivated and qualified intern to conduct research on machine learning based electric motor design and optimization. Ideal candidates should be Ph.D. students with a solid background and publication record in electric machine design, optimization, and machine learning. Hands-on experience with the implementation of optimization algorithms, machine learning and deep learning methods is required. Strong programming skills using Python/PyTorch are expected. Knowledge and experience with electric machine principle, design and finite-element analysis are highly desirable. Start date for this internship is flexible and the duration is 3-6 months.

The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop algorithms that can perform background oriented Schlieren (BOS) tomography. The project goal is to utilize both analytical and learning-based architectures to enable the reconstruction of 3D air flows in an indoor setting from BOS measurements. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, large-scale optimization, learning-based modeling for imaging, Schlieren tomography, physics informed neural networks. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.

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.

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, model-based learning, and optimization. Expertise in joint waveform/sequence optimization, integrated ISAC precoder/combiner design, model-based learning for ISAC, and downlink/uplink/active sensing under timing and frequency offsets is highly desired. Familiarity with IEEE 802.11 (ac/ax/ad/ay) standards is a plus but not required. 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 Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in 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 indoor/outdoor radar 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.

MERL is seeking a highly motivated and qualified intern to carry out research on energy efficient IoT network design methodology. The candidate is expected to develop innovative network technologies to support energy efficient communications among heterogeneous nodes. The candidates should have knowledge of network technologies such as sleep management and path planning and network simulation tools such as ns3. Knowledge of communication standards such as IEEE 802.11 and 3GPP and UAV 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.

MERL is seeking a highly motivated and qualified intern to conduct research on emerging next generation IoT networking. The candidate is expected to develop innovative networking technologies to achieve efficient network traffic delivery in next generation IoT networks with heterogeneous nodes. The candidate should have knowledge of networking protocols such as multi-path UDP/TCP and network simulation tools such as ns3. Knowledge of the optimization such as optimal network path discovery 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.

MERL is looking for a highly motivated and qualified individual to work on optimization-based algorithms for coordination, scheduling, and motion planning of automated ground robots in uncertain surrounding environments. The ideal candidate should have experience in either one or multiple of the following topics: formulation of mixed-logic constraints as mixed-integer programs, connected vehicles and coordination, inter-robot collision avoidance, multi-agent scheduling, and traffic control systems. Prior experience with one or multiple traffic and/or multi-vehicle simulators (e.g., SUMO, CarSim, CARLA, etc.) is a plus. PhD students in engineering or mathematics, especially with a focus on research related to any of the above topics 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 required; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3 months, and 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 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 looking for a highly motivated and qualified individual to work on tailored computational algorithms for optimization-based motion planning and predictive control applications in autonomous systems (vehicles, mobile robots). The ideal candidate should have experience in either one or multiple of the following topics: convex and non-convex optimization, stochastic predictive control (e.g., scenario trees), interaction-aware motion planning, machine learning, learning-based model predictive control, mathematical programs with complementarity constraints (MPCCs), optimal control, and real-time optimization. PhD students in engineering or mathematics, especially with a focus on research related to any of the above topics 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 required; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3 months, and the start date is flexible.

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, statistical estimation and hypothesis testing, reinforcement learning, and planning and control of aerial and ground robots. The candidate should be proficient in Python. Additional knowledge of ROS and C/C++ and demonstrable experience in ground and aerial robots are a plus. The minimum duration of the internship is 3 months; the start time is Summer/Fall 2024.

MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on control and planning algorithms for legged robots for support activities of and collaboration with humans. The ideal candidate is expected to be working towards a PhD with strong emphasis in robotics control and planning and to have interest and background in as many as possible of: motion planning algorithms, control for legged robot locomotions, legged robots, perception and sensing with multiple sensors, SLAM, vision-based control. Good programming skills in Python or C/C++ are required. The expected start of of the internship is flexible, with 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 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 flexible, with duration of 3--6 months.

MERL is looking for a highly motivated individual to develop and validate map building algorithms for autonomous mobile robots. The ideal candidate will have published in one or more of these topics: planning and control of ground robots, map building, (visual) SLAM, and sensor fusion. The candidate should be proficient in ROS and C/C++, familiar with Python, and has demonstrable experience working with mobile robots. The minimum duration of the internship is 3 months; the start time is Summer/Fall 2024.

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 trajectory generation and motion planning for heterogenous teams of mobile robots, including drones and ground vehicles, with performance and safety guarantees. The ideal candidate is expected to be working towards a PhD with strong emphasis in planning and control, and to have interest and background in as many as possible of: predictive control algorithms for linear and nonlinear systems, set-based methods in control (reachability, invariance), stochastic control for uncertain systems, SLAM and vision-based planning and control. Good programming skills in MATLAB, Python or C/C++ are required. The expected start of of the internship is flexible, with duration of 3--6 months.

MERL is seeking a highly motivated and qualified individual to conduct research on DER-centric Microgrid Optimization and Control. Ideal candidate should be a senior Ph.D. student with solid background and publication record in any of the following, or related areas: power systems, Microgrid, distribution energy resources, host capacity, optimization, and uncertainty modelling. Hand-on experience on programming using Python, C/C++, or MATLAB is required. The duration of the internship is anticipated to be 3-6 months, and the start date is flexible.

MERL is looking for a self-motivated and qualified candidate to work on Bayesian Optimization algorithms applied to 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. Proficiency in Python is required. An expected outcome of the internship is one or more peer-reviewed publications. The expected duration is 3-4 months, with flexible starting date.

MERL is looking for a highly-motivated individual to work on estimation and control for dexterous contact-rich manipulation using vision and tactile feedback. The research will develop novel estimation and control algorithms for dexterous manipulation tasks such as bimanual assembly and tool manipulation under unexpected contact events using optimization, machine learning, and contact sensing. The ideal candidate should have experience in either one or multiple of the following topics: Visuo-tactile manipulation, (learning-based) contact estimation, in-hand manipulation, tactile perception, computer vision, trajectory optimization through contact, and deep reinforcement learning for contact-rich systems. Senior PhD students in robotics, computer science, and engineering with a focus on robotic manipulation and legged robotics are encouraged to apply. Prior experience working with physical robotic systems with closed-loop control is required as results need to be implemented on a physical hardware. A successful internship will result in submission of results to peer-reviewed conference (e.g., ICRA, IROS, RSS, CoRL) and journals (e.g., TRO, IJRR). Good coding skills in Python and C++ are required. The expected duration of internship is 3-4 months with flexible start dates. This internship is preferred to be onsite at MERL.

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: conic programming, active-set methods, and nixed integer programs. Strong programming skills and fluency in C++ and 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.

MERL is looking for a self-motivated intern to develop algorithms for time series data analytics, system identification, and deep learning. The ideal candidate would be a senior PhD student with experience in one or more of the following areas: system identification, deep learning, mathematical optimization. Strong programming skills using Python/PyTorch are expected. Experience in developing heuristics for nonconvex optimization problems and building spatial-temporal prediction models would be 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.

MERL is looking for a highly motivated and qualified candidate to work on estimation of multi-physical systems governed by sets of differential algebraic equations (DAEs). The research will involve study and development of estimation approaches for large-scale nonlinear systems, e.g., vapor compression cycles, with limited sensor availability. The ideal candidate will have a strong background in one or multiple of the following topics: nonlinear control and estimation, sensor selection, optimization, and active 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 the 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.

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.