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 seeking a highly motivated and qualified intern to collaborate with the Multiphysical Systems (MS) team in research on knowledge transfer methods for deep learning, e.g. meta/transfer learning to be used for system identification using data from real building energy systems. The ideal candidate is expected to be working towards a Ph.D. in applying deep learning to system identification problems, with special emphasis in one or more of: generative modeling, probabilistic deep learning, and learning for estimation/control. Fluency in PyTorch is necessary. Previous peer-reviewed publications in related research topics and/or experience with state-of-the-art generative models for time-series is a plus. Publication of results obtained during the internship is expected. The minimum duration of the internship is 12 weeks; start time is flexible with slight preference towards late Spring/early Summer 2024.

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.

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 2024. Knowledge of data-driven modeling techniques is required. Experience with sampling-based control methods is preferred. Experience in working with thermo-fluid systems is a plus. 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.

MERL is seeking a highly motivated and qualified individual to conduct research in dynamic model-based robotic system design and control. The ideal candidate should demonstrate solid research record in robotic dynamics and differentiable simulation, motion planning and control, and optimization. Strong coding skill on implementing robotic dynamics and differentiable simulation/optimization using CasADi/PyTorch 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 seeking a highly motivated and qualified individual to conduct research in the intersection of control theory and learning to achieve high precision motion with guaranteed safety and robustness. The ideal candidate should have solid backgrounds in mechanics, uncertainty quantification, control theory, and reinforcement learning, and strong coding skills. Prior experience on ultra-high precision motion control system and visual servoing 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.

MERL is seeking a highly motivated candidate to collaborate with the Control for Autonomy team in research on developing estimation algorithms for GNSS positioning. The ideal candidate is expected to be working towards an MSc or PhD with emphasis on GNSS positioning, and it is a merit to have interest and background in one or several of: factor graph optimization, statistical estimation theory, inertial navigation systems, Kalman filtering, hands-on experience with RTKlib or similar software. Good programming skills in MATLAB are required and knowledge of C is a merit. The expected duration of the internship is 3 months with a start date of late spring or early summer of 2024.

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 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 candidate to collaborate with the Control for Autonomy team in research on control and estimation for quadrotors. The ideal candidate is expected to be working towards a PhD with emphasis on control or related areas, and it is a merit to have interest and background in one or several of: experimentation and research on quadrotors in general and Crazyflies in particular, Lyapunov stability theory, statistical estimation theory, and visual-inertial SLAM. Good programming skills in MATLAB, ROS, Python, are required and knowledge of C is a merit. The expected duration of the internship is 3 months with a start date of late spring or early summer of 2024.

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 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 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 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 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 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.

The Dynamics team at MERL is seeking a highly motivated, qualified individual to join our internship program in the summer of 2024. The ideal candidate will be a Ph.D. student specializing in engineering, applied mathematics, computer science or similar fields with solid background in estimation, control and dynamical systems theory. 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.

The Dynamics team at MERL is seeking a motivated and qualified individual to conduct research in safe robust reinforcement learning (RL). The ideal candidate should have solid background in RL, e.g. Constrained Markov decision processes (CMDPs), and Robust MDPs theories. Knowledge of dynamical system theory and nonlinear control theory is a plus, but not a requirement. Submission of the results produced during the internship is anticipated, e.g., ICML/ICLR/NeurIPS. Duration of the internship is expected to be 3 months. Start date is flexible.

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 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.