Posting Title

Graduate PhD Intern (Summer) – Reduced-Order Modeling for Turbulent Flows

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Location

CO - Golden

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Position Type

Intern (Fixed Term)

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Hours Per Week

40

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Working at NREL

The National Renewable Energy Laboratory (NREL), located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for research and development of renewable energy and energy efficiency technologies.

Join a team of world-class scientists, engineers, and visionaries dedicated to shaping the world’s energy future through cutting-edge research and innovation. From our vision to our NREL community, we are unique in the research community. We are focused on impact. From our work in basic sciences to systems engineering, analysis, demonstration, and deployment, we are focused on solving market-relevant problems that result in advanced, secure, reliable, and affordable energy systems. We are trusted clean energy leaders, developing cost-saving solutions that make U.S. industries more competitive, and support job creation and economic growth across rural and urban communities.

At NREL, we offer a unique, mission-driven work environment with cutting-edge facilities and multidisciplinary research teams. NREL's environment offers strong partnerships with industry, academia, and other national laboratories, as well as professional development opportunities and a competitive benefits package for employees.

Learn about NREL’s critical objectives: NREL's Mission and Vision.

Job Description

The Data, Analysis, and Visualization group in NREL’s Computational Science Center has an immediate opening for a Graduate Student Researcher to pursue a cross-disciplinary research project in mathematical methods, HPC, and machine and statistical modeling with an emphasis on the development of reduced-order models for turbulent flows.

The successful candidate will work with a research team to develop data-driven reduced-order methods for turbulent flow data. The candidate will validate modeling results with appropriate statistical procedures and metrics and will co-author the publication of our results.

The candidate will evaluate and communicate results through written research reports and presentation, will participate in group meetings and seminars, and will assist in engineering and writing associated software, analysis of results, algorithm design, and publication development.

Daily responsibilities will include computer programming, post-processing data, developing data driven reduced order models, and validating their results.

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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

Experience with:

• Reduced-order modeling / Dimension reduction
• Numerical linear algebra
• High performance computing (HPC)
• Scripting and workflow management: Python, or similar languages.

Only current PhD students will be considered for this position.

Preferred Qualifications

Preference will be given to candidates with a strong interest in data-driven methods, chaotic dynamical systems, and post-processing.

Experience in the following would be highly advantageous:

• Courses in computer science, and machine learning;
• UNIX computational environments;