Research Aide - MCS - Fang, Lin - 5.20.26.
About The Position
This project investigates the sensitivity of power-grid reliability and operational metrics to generation and transmission outages under uncertainty. The work will focus on quantifying how localized component failures propagate through network constraints and affect system-level outcomes such as congestion, load shedding risk, reserve adequacy, and probabilistic reliability measures (e.g., LOLP/LOLE). The student or researcher will develop computational experiments using large-scale optimization and simulation tools, with an emphasis on scalable ACOPF and stochastic modeling workflows. Particular attention will be paid to correlated outages, weather-driven stress conditions, and uncertainty propagation across multi-period operational settings. The project may also explore surrogate or machine learning models to accelerate sensitivity estimation and rare-event analysis on large grid ensembles. The overall goal is to better understand which outage patterns most strongly influence grid resilience and how uncertainty in outage behavior impacts operational decision-making.
Requirements
- Currently enrolled in undergraduate or graduate studies at an accredited institution.
- Graduated from an accredited institution within the past 3 months.
- Actively enrolled in a graduate program at an accredited institution.
- Must be 18 years or older at the time the appointment begins.
- Must possess a cumulative GPA of 3.0 on a 4.0 scale.
- Must complete a satisfactory background check.
- All students remain subject to applicable drug testing policies.
- The entirety of the appointment must be conducted within the United States.
Responsibilities
- Develop computational experiments using large-scale optimization and simulation tools.
- Emphasize scalable ACOPF and stochastic modeling workflows.
- Pay particular attention to correlated outages, weather-driven stress conditions, and uncertainty propagation across multi-period operational settings.
- Explore surrogate or machine learning models to accelerate sensitivity estimation and rare-event analysis on large grid ensembles.
Benefits
- Comprehensive benefits are part of the total rewards package.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
No Education Listed
