Visiting Student-Subcontractor - MCS -Memon, Zeeshan - 9.17.26.

This project will investigate how complex power system dynamics can be learned from large-scale simulation and operational data to enable predictive models that generalize across operating conditions, disturbances, and network configurations. The long-term goal is to develop foundation models for power system dynamics that can provide reusable representations of system behavior and support a broad range of downstream tasks, including dynamic prediction, stability assessment, contingency analysis, risk estimation, and control.

The project will explore how representations of power system state and dynamics can be learned that preserve the information required to predict future system behavior while remaining transferable across operating points, disturbances, and network structures. Particular attention will be given to learning representations capable of describing evolving dynamical regimes, representing interactions between continuous and discrete phenomena, and supporting prediction beyond the specific trajectories and configurations encountered during training.

The ultimate objective is a general-purpose learned model of power system dynamics that can serve as a common predictive backbone for many analysis and decision-making problems. Such a model would complement high-fidelity numerical simulation by enabling rapid prediction, transfer across related systems and scenarios, and the reuse of learned dynamical knowledge across multiple power-system applications.

The student will contribute to the development and evaluation of generalizable learned models of power system dynamics by generating and analyzing large-scale dynamic simulation data, developing computational workflows for representing system states and network configurations, and evaluating model performance across different operating conditions, disturbances, and topologies. The student will investigate challenges associated with differential-algebraic dynamics, multiple time scales, nonsmooth and switching behavior, rare or unstable trajectories, and generalization beyond the training distribution. Responsibilities will also include designing numerical experiments and benchmarks, analyzing model limitations and failure modes, maintaining research software and data pipelines, and contributing to the documentation, presentation, and publication of research results.

Education and Experience Requirements

• The entirety of the appointment must be conducted within the United States. 

• Must be 18 years or older at the time the appointment begins.

• Applicants must be:

o Currently enrolled in undergraduate or graduate studies at an accredited institution. 

o Graduated from an accredited institution within the past 3 months; or

o Actively enrolled in a graduate program at an accredited institution. 

Job Family

Visiting Student Graduate

Job Profile

Visiting Student Subcontract - Graduate

Worker Type

Contingent Worker

Time Type

Part time

Scheduled Weekly Hours

20

EEO Information

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