Graduate; Year-Round) Internship - Digital Twin Development Biorefinery Processes

Posting Title

Graduate (Year-Round) Internship
- Digital Twin Development for Biorefinery Processes

CO
- Golden

Intern (Fixed Term)

40

NLR is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development. Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future.

Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth. At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories.

We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.

The Integrated Carbon Conversion Processes (ICCP) Group within NREL's Catalytic Carbon Transformation and Scale-Up (CCTS) Center has an opening for a Graduate Internship position. The selected candidate will support senior process engineers, modelers, and data scientists in developing a digital twin for an experimental biological and thermocatalytic conversion platform. The goal of the project is to create a virtual representation of key unit operations, enabling real-time monitoring, dynamic simulation, and predictive analytics for biomass and waste conversion to produce biofuels and bioproducts.

• Design and implement process models representing biomass conversion, upgrading, and separation units
• Integrate sensor data and historical process data into the digital twin architecture
• Develop simulation tools and dashboards for visualization, control, and scenario analysis
• Contribute to model validation using pilot-scale data and collaboration on experimental feedback loops
• Document assumptions, system architecture, and modeling workflows for reproducibility and team collaboration
• Participate in team meetings and present regular progress updates
• Contribute toward peer reviewed manuscripts and other technical documentation

• 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.

• The candidate should be currently pursuing or have recently completed a master's degree or be currently enrolled in a Ph.D. program in computational sciences, computational engineering, mechanical engineering, chemical engineering, biological engineering, chemistry, biology, or a related field
• Experience programming in Python and/or C++ Experience modeling chemical reactors (e.g., using Cantera) and integrating with computational fluid dynamics (CFD) frameworks

• Experience building techno-economic models using software platforms (e.g., Aspen Plus)
• Exposure to artificial intelligence/machine learning methods for process optimization or anomaly detection
• Experience with digital twin platforms (e.g., Any Logic, TwinCAT, Siemens Xcelerator)
• Interest in bioprocessing, energy systems, or sustainable technology development.
• Strong problem-solving, communication, and…