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Posting Title Graduate PhD (summer/year-round) Intern - AI Foundation Model for Power System Optimization . Location CO - Golden . Position Type Intern (Fixed Term) . Hours Per Week 40 . Working
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, the goal is to solve large-scale nonlinear problems efficiently while preserving theoretical guarantees. The intern will contribute to developing scalable learning-based optimization methods for next
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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
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to write clear and concise policies, training documents, investigative/formal reports, and operational documents. Background and knowledge related to SCI regulations, facilities, and SCI control systems
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metrics for large-scale, unbalanced distribution networks. Designing and implementing advanced dynamic state estimation algorithms (EKF, UKF, MHE, WLS-based methods) for measurement-constrained distribution
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studies Perform side-by-side comparisons between EMT and RMS models to assess accuracy and applicability for different planning scenarios Document modeling methods, assumptions, and validation results in a
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benefits package designed to support your career and well-being. Job Description This employee will collect the required data and develop fundamental methods and software tools and interfaces required
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system stability using small- and large-signal stability assessment methods and potentially develop a new stability framework for improvement • Develop module-level control and converter-level
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to evaluate transit performance and accessibility. Deliver quality products that synthesize external literature, data analyses, and modeling results. Document methods and assumptions and assist with preparing
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converters interfacing various MW-scale distributed energy resources. • Develop advanced control methods for high-power converters at different timescales and layers, such as grid-forming control