Internship: Optimization based Control and Estimation for Autonomous Vehicles

Cambridge, Massachusetts, United States · ME · 1183

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


Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

MERL expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of MERL’s employees to perform their job duties may result in discipline up to and including discharge.

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