Graduate PhD (summer/year-round) Intern - AI Foundation Model for Power System Optimization
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About this role
The National Lab of the Rockies (NLR) is thrilled to announce an exciting opportunity for a full-time Ph.D. intern with experience in machine learning, generative AI, foundation model, large language model, power system optimization, graphic neural network for power system modeling. This is your chance to be at the forefront of energy innovation, working alongside a dynamic, multidisciplinary team of experts from NLR and its collaborators. As a graduate intern within the Sensing and Predictive Analytics Group within the Power System Engineering Center (PSEC), you will dive into a groundbreaking project, developing and implementing cutting-edge AI foundation models for power system optimization problems. Your primary mission will be to leverage your expertise in AI foundation models and power system optimization to revolutionize power system operation and planning. Your deep knowledge of generative AI, AI foundation model, graphic neural networks, and power system optimization will be the driving force behind your success in this role. This full-time position offers the flexibility of optional remote work.
Key Responsibilities:
- Innovate and Optimize: Build best-in-class AI foundation models and generative AI models for power system optimization, using RNN, GAN, and LLMs.
- Implement and Impact: Bring your algorithms to life for industry partners, making tangible improvements in learn to optimize domain.
- Lead and Collaborate: Manage our project GitHub repository for experiment tracking and code versioning, ensuring seamless collaboration with partners and code excellence.
- Share Your Discoveries: Present your groundbreaking results and key findings at workshops, conferences, and in high-quality journals, positioning yourself as a thought leader in the field.
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.
- Must meet educational requirements prior to employment start date.
Additional Required Qualifications
- A current PhD candidate in EE, Computer Science, Computer Engineering, Applied Math, or a related analytical domain.
- 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.
Preferred Qualifications
- Hands-on experience in foundation model, generative AI, and LLMs.
- Experience in power system optimization.
- Knowledge about power system operation and planning.