Internship - Data-Driven Estimation and Control for Spatiotemporal Dynamics
About The Position
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.
Requirements
- PhD student in engineering, computer science, or related fields
- strong background in estimation, control, and dynamical systems theory
Nice To Haves
- knowledge of reinforcement learning
- knowledge of reduced-order modeling (ROM)
- knowledge of partial differential equations (PDEs)
Responsibilities
- develop novel algorithms
- conduct numerical experiments
- prepare results for publication
Benefits
- relocation stipend
- covered travel to and from MERL
- monthly Charlie Card for local commuting
- weekly social gatherings
- professional development opportunities, including research talks by both internal and external speakers
- health insurance coverage (after 90-day waiting period)
- immigration support for qualified candidates
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What This Job Offers
Career Level
Intern
Industry
Professional, Scientific, and Technical Services
Education Level
Ph.D. or professional degree
Number of Employees
101-250 employees
