PhD intern developing cutting-edge AI foundation models for power system optimization at National Laboratory of the Rockies. Collaborating with experts in a mission-driven environment.
Responsibilities
Build best-in-class AI foundation models and generative AI models for power system optimization, using RNN, GAN, and LLMs.
Bring your algorithms to life for industry partners, making tangible improvements in learn to optimize domain.
Manage our project GitHub repository for experiment tracking and code versioning, ensuring seamless collaboration with partners and code excellence.
Present your groundbreaking results and key findings at workshops, conferences, and in high-quality journals, positioning yourself as a thought leader in the field.
Requirements
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.
Expertise in Python and its related libraries, such as Tensorflow, Hugging Face, and Pytorch.
Proven experience in power system optimization and AI foundation models.
A comprehensive understanding of learn to optimize topic.
A track record of publishing high-quality research papers.
Benefits
medical, dental, and vision insurance
403(b) Employee Savings Plan with employer match*
sick leave (where required by law)
Job title
Graduate PhD Intern – AI Foundation Model for Power System Optimization
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