Graduate PhD Student (Year-Round) Machine Learning Applications for Cyber-Physical Power System Operations Intern

Job Description

This position is looking for a year-round intern (summer intern is also acceptable) who has strong technical background in machine learning (ML) and artificial intelligent (AI) applications in cyber-physical power systems, ideally on the distribution grid with distributed energy resources (DERs). The intern will work on projects developing learning-based analytics, and cyber-attack-resilient control strategies and optimizations for power distribution systems. The intern will also be responsible for conducting literature review, identifying gaps and summarizing new research opportunities. The ideal candidate should be able to conduct research work independently and also collaborate with project PI and other researchers from the project team.

Basic Qualifications

• Minimum of a 3.0 cumulative grade point average.
• Undergraduate: Must be enrolled as a full-time student in a bachelor’s degree program from an accredited institution. Post Undergraduate: Earned a bachelor’s degree within the past 12 months. Eligible for an internship period of up to one year.
• Graduate: Must be enrolled as a full-time student in a master’s degree program from an accredited institution. Post Graduate: Earned a master’s degree within the past 12 months. Eligible for an internship period of up to one year.
• Graduate + PhD: Completed master’s degree and enrolled as PhD student from an accredited institution.

Please Note:

• Applicants are responsible for uploading official or unofficial school transcripts, as part of the application process.
• If selected for position, a letter of recommendation will be required as part of the hiring process.
• Must meet educational requirements prior to employment start date.

Additional Required Qualifications

• 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

Preferred Qualifications

• Experience in using high performance computers, Linux systems