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and test Learning-to-Optimize (L2O) algorithms for accelerating large-scale optimization. Perform computational experiments on benchmark power system datasets. Participate in weekly research meetings
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Computer Science or related fields Experience in power system modeling, optimization, and control Experience in at least one of the power system software tools including OpenDSS, PSCAD, PSS/E Experience in at least
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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
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Posting Title Graduate PhD Student Intern (Summer) – Mathematical Optimization . Location CO - Golden . Position Type Intern (Fixed Term) . Hours Per Week 40 . Working at NLR NLR is located
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motivated Postdoctoral Researcher to join our Grid Planning and Analysis Center (GPAC). This position will focus on tackling critical optimization challenges in medium-term power system planning like
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focuses on developing RIC rApps, xApps, and dApps, along with NWDAF-based analytics, enabling closed-loop automation and AI-powered optimization across the RAN and core network. Interns will work on cloud
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languages like Python or Julia, including at least one programming language relevant to simulation, data analysis, visualization, optimization, and/or high-performance computing Strong writing, interpersonal
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. The successful candidate will collaborate with NREL staff and researchers to design and implement quantum reservoir computing algorithms for power systems analysis and optimization toward the goal of ascertaining
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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
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developing optimization and control models of power systems, distributed energy resources (DERs), and validating controls using hardware-in-the-loop testing facility. We are looking to hire an intern to work