Internship: Optimization based Control and Estimation for Autonomous Vehicles

Cambridge, Massachusetts, United States · ME · 1183 expand job description ↓


MERL is looking for highly motivated individuals to work on the development and implementation of optimization based control and estimation algorithms, including model predictive control (MPC) and/or moving horizon estimation (MHE), for autonomous vehicles. The research will involve some among the following: the study and development of nonlinear optimization techniques for real-time optimal control, the implementation of algorithms for predictive control or state and parameter estimation and the validation of techniques using embedded control hardware and/or experimental data. The ideal candidate should have experience in optimization algorithms, model predictive control and moving horizon estimation, nonlinear vehicle dynamics, ADAS and vehicle control or estimation. PhD students in engineering or mathematics with a focus on optimization algorithms, nonlinear MPC/MHE or vehicle control and estimation are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in Matlab is expected; 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.

Research Area: Mechatronics

Contact: Rien Quirynen

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Are you qualified to apply for an internship at MERL? Qualified applicants for MERL internships are individuals who have or can obtain full authorization to work in the U.S. and do not require export licenses to receive information about the projects they will be exposed to at MERL. The U.S. government prohibits the release of information without an export license to citizens of several countries, including, without limitation, Cuba, Iran, North Korea, Sudan and Syria (Country Group E:1 of Part 740, Supplement 1, of the U.S. Export Administration Regulations).
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