Internship: Optimization Algorithms for Motion Planning and Predictive Control
Description For Internship: Optimization Algorithms for Motion Planning and Predictive Control
MERL is seeking a highly motivated and qualified individual for an internship focused on developing 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 one or more of the following areas: convex and non-convex optimization, stochastic predictive control, interaction-aware motion planning, machine learning, learning-based model predictive control, mathematical programs with complementarity constraints (MPCCs), optimal control, and real-time optimization.
This internship is particularly suited for PhD students in engineering or mathematics with research focus on related topics. The intern is expected to publish relevant results in conference proceedings or journals. Strong implementation skills in MATLAB/Python are required, with C/C++ coding experience being a plus.
Key Details:
- Duration: 3 months
- Start Date: Flexible
- Location: Cambridge, Massachusetts, United States (On-site)
Research Areas:
- Control
- Dynamical Systems
- Machine Learning
- Optimization
- Robotics
The intern will work on cutting-edge projects in autonomous systems, contributing to the development of advanced algorithms for motion planning and predictive control. This is an excellent opportunity to gain hands-on experience in a leading research laboratory and make meaningful contributions to the field of autonomous systems.
MERL provides equal employment opportunities and prohibits workplace harassment. The internship requires full authorization to work in the U.S. and may be subject to export control restrictions.
Join MERL to push the boundaries of technology in a collaborative and innovative environment!
Responsibilities For Internship: Optimization Algorithms for Motion Planning and Predictive Control
- Develop tailored computational algorithms for optimization-based motion planning
- Work on predictive control applications for autonomous systems (vehicles, mobile robots)
- Implement designs and algorithms in MATLAB/Python
- Conduct research in relevant areas such as convex optimization, machine learning, or optimal control
- Publish research findings in conference proceedings or journals
- Collaborate with MERL researchers on cutting-edge projects in autonomous systems
Requirements For Internship: Optimization Algorithms for Motion Planning and Predictive Control
- PhD student in engineering or mathematics (preferred)
- Experience in one or more: convex and non-convex optimization, stochastic predictive control, interaction-aware motion planning, machine learning, learning-based model predictive control, MPCCs, optimal control, real-time optimization
- Strong implementation skills in MATLAB/Python
- Ability to publish research results in conference proceedings or journals
- Full authorization to work in the U.S.
- C/C++ coding experience (preferred)
Benefits For Internship: Optimization Algorithms for Motion Planning and Predictive Control
- Opportunity to work in a leading research laboratory
- Exposure to cutting-edge technologies in autonomous systems
- Potential for publishing research findings
- Collaboration with experienced researchers
