Graduate PhD Student – Machine Learning Applications for Cyber-Physical Power System Operations Intern
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Job Description
Graduate PhD Student intern engaged in machine learning applications for cyber-physical power system operations. Collaborating on developing analytics and control strategies in energy systems.
Responsibilities:
- looking for a year-round intern (summer intern is also acceptable)
- work on projects developing learning-based analytics
- cyber-attack-resilient control strategies and optimizations for power distribution systems
- conducting literature review
- identifying gaps and summarizing new research opportunities
- conduct research work independently
- collaborate with project PI and other researchers from the project team
Requirements:
- Minimum of a 3.0 cumulative grade point average
- Must be enrolled as a full-time student in a bachelor’s degree program from an accredited institution
- Earned a bachelor’s degree within the past 12 months
- Must be enrolled as a full-time student in a master’s degree program from an accredited institution
- Earned a master’s degree within the past 12 months
- Completed master’s degree and enrolled as PhD student from an accredited institution
- Currently enrolled Ph.D. students in power systems engineering, electrical engineering, or other relevant areas
- Have a good fundamental knowledge of neural networks, state-of-the-art learning algorithms, and their applications to complex systems
- Experienced in one or more ML/AI techniques, such as reinforcement learning, federated learning, natural language learning, graph neural network
- Experience working on cyber-physical power systems, ideally on the distribution grid with distributed energy resources (DERs) and modeling of cyber/network components
- Proficiency in using Python
Benefits:
- medical, dental, and vision insurance
- 403(b) Employee Savings Plan with employer match*
- sick leave (where required by law)
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