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CA0153: Internship - High-Fidelity Visualization and Simulation for Space Applications
MERL is seeking a highly motivated graduate student to develop high-fidelity full-stack GNC simulators for space applications. The ideal candidate has strong experience with rendering engines, synthetic image generation, and computer vision, as well as familiarity with spacecraft dynamics, motion planning, and state estimation. The developed software should allow for closed-loop execution with the synthetic imagery, and ideally allow for real-time visualization. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.
Required Specific Experience
- Current enrollment in a graduate program in Aerospace, Computer Science, Robotics, Mechanical, Electrical Engineering, or a related field
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Experience with one or more of Blender, Unreal, Unity, along with their APIs
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Strong programming skills in one or more of Matlab, Python, and/or C/C++
The pay range for this internship position will be6-8K per month.
- Research Areas: Computer Vision, Control, Dynamical Systems, Optimization
- Host: Avishai Weiss
- Apply Now
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CA0178: Internship - Planning and Control of Multi-robot systems
MERL is seeking a highly motivated intern to collaborate in the development decision making, planning and control for teams of ground robot in task such as coverage control, monitoring and pursuit-evasion. The ideal candidate is a PhD student with strong experience in planning and control of multi-agent systems, with background in advanced model-based (e.g., MPC) and learning-based (e.g., RL) methods. The results of the internship are expected to be published in top-tier conferences and/or journals. The internship will take place during Fall/Winter 2025 (exact dates are flexible) with an expected duration of 3-6 months.
Please use your cover letter to explain how you meet the following requirements, preferably with links to papers, code repositories, etc., indicating your proficiency.
Required Experience
- Current enrollment in a PhD program in Mechanical, Electrical, Aerospace Engineering, Computer Science or related programs, with a focus on Robotics and/or Control Systems
- Experience in as many as possible of:
- Formal methods and set based methods (temporal logics, reachability, invariance)
- Model predictive control (design, analysis, solvers)
- Reinforcement learning for planning
- Cooperative planning and control for multi-agent systems
- Programming in Python or Matlab or Julia
Additional Useful Experience
- Knowledge of one or more physics simulators for robotics (e.g., MuJoco)
- Experience with coverage control and pursuit-evasion problems
- Programming in C/C++ or Simulink code generation
The pay range for this internship position will be6-8K per month.
- Research Areas: Control, Dynamical Systems, Robotics
- Host: Stefano Di Cairano
- Apply Now
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OR0217: Internship - Fast Electromagnetic Transient Analysis of Power Grids
Mitsubishi Electric Research Laboratories (MERL) is seeking a highly motivated Ph.D. student intern to conduct research on electromagnetic transient analysis of power grids. The ideal candidate will have a strong background in power systems, transient analysis, dynamic simulation, converter control, numerical methods, and optimization. Proficiency in MATLAB, Python, or C++ is required. Prior experience with electromagnetic transient analysis will be considered a plus. This internship is expected to last three to six months, with a flexible start date. The pay range for this internship position will be 6-8K per month.
- Research Areas: Control, Data Analytics, Dynamical Systems, Electric Systems, Optimization
- Host: Hongbo Sun
- Apply Now
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MS0098: Internship - Control and Estimation for Large-Scale Thermofluid Systems
MERL is seeking a motivated graduate student to research methods for state and parameter estimation and optimization of large-scale systems for process applications. 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 control and estimation, numerical methods, 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.
The pay range for this internship position will be 6-8K per month.
- Research Areas: Optimization, Machine Learning, Control, Multi-Physical Modeling
- Host: Chris Laughman
- Apply Now
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CI0190: Internship-IoT Network Methodology
MERL is seeking a highly motivated and qualified intern to carry out research on UAV assisted IoT network methodology. The candidate is expected to develop innovative path planning technologies to support UAV swarm navigation in IoT network environments. The candidates should have knowledge of communication network technologies such as path planning and cooperative network operations. Knowledge of control technology and path management is a plus. Start date for this internship is flexible and the duration is about 3 months.
Responsibilities for this position include:
- Research on UAV assisted IoT networks
- Develop path planning technologies to support UAV coordination in IoT networks
- Simulate and analyze the performance of developed technology
Qualifications for this position are:
- Junior and senior year Ph.D students
The pay range for this internship position will be 6-8K per month.
- Research Areas: Communications, Control, Dynamical Systems, Machine Learning, Optimization, Robotics, Signal Processing
- Host: Jianlin Guo
- Apply Now