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National Laboratory of the Rockies · 1 day ago

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

Golden, CO

Internship

Onsite

Intern

$51K/yr - $82K/yr

National Laboratory of the Rockies (NLR) is the nation's primary laboratory for energy systems research and development. They are seeking a year-round intern with a strong technical background in machine learning and artificial intelligence applications for cyber-physical power systems, focusing on developing learning-based analytics and control strategies for power distribution systems.

Clean EnergyCleanTechEnergyRenewable Energy

Responsibilities

Develop learning-based analytics and cyber-attack-resilient control strategies and optimizations for power distribution systems

Conduct literature review, identifying gaps and summarizing new research opportunities

Collaborate with project PI and other researchers from the project team

Qualification

Machine LearningArtificial IntelligenceCyber-Physical SystemsPythonNeural NetworksReinforcement LearningDistributed Energy ResourcesResearch SkillsCollaboration

Required

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

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