Massachusetts Institute of Technology
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6 days ago
Postdoctoral Researcher in Networked Systems, Power-Grid Topology Control, and Reinforcement Learning at MIT Massachusetts Institute of Technology in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Deadline
Nov 30, 2026
Country
United States
University
Massachusetts Institute of Technology

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About this position
MIT is advertising a postdoctoral researcher opportunity in networked systems, power-grid topology control, and reinforcement learning. The research sits at the intersection of multi-agent systems, control, optimization, and learning for complex systems, with applications to large-scale power-grid decision-making.
The postdoc will work on challenging problems such as distributed coordination across many interacting components, non-stationary operating conditions, and actions that must satisfy strict physical and safety constraints. Possible directions include scalable and decentralized decision-making, robust learning under partial observability, safety and constraint satisfaction, online adaptation to changing network conditions, and learning-based methods for nonlinear optimization and control.
The technical agenda is intentionally open, and the position allows substantial freedom to shape the research direction. The role includes close collaboration with the MIT research team and collaborators, and may involve both theoretical work and large-scale computational studies.
Institution: Massachusetts Institute of Technology (MIT)
Position: Postdoctoral Researcher
Research areas: Networked systems, power-grid topology control, reinforcement learning, multi-agent systems, control, optimization
Application: Rolling review and rolling interviews. Early applications are encouraged. The post says the team aims to fill the position by the end of November 2026.
Apply here: https://lnkd.in/eqbYNw6w
Funding details
Available
What's required
Applicants should have strong preparation in multi-agent systems, control, optimization, learning, or related areas. The post emphasizes interest in scalable and decentralized decision-making, robust learning under partial observability, safety and constraint satisfaction, online adaptation, and learning-based approaches to nonlinear optimization and control. The role is open-ended and suitable for candidates who can contribute both theoretically and computationally.
How to apply
Apply through the linked application page. Applications are reviewed on a rolling basis, so early submission is encouraged. Interviews will also be conducted on a rolling basis.
More information can be found here
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