Internship - Data-Driven Estimation and Control for Spatiotemporal Dynamics

Job description

MERL is seeking an intern to work on data-driven estimation and control for spatiotemporal dynamical systems, with applications in indoor airflow optimization. The ideal candidate would be a PhD student in engineering, computer science, or related fields with a strong background in estimation, control, and dynamical systems theory. Preferred skills include knowledge of reinforcement learning, reduced-order modeling (ROM) and partial differential equations (PDEs). The intern will work closely with MERL researchers to develop novel algorithms, conduct numerical experiments, and prepare results for publication. The duration is expected to be at least 3 months with a flexible start date. The pay range for this internship position will be 6-8K per month. 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...