Graduate Digital Twin Internship for Biorefinery

National Laboratory of the Rockies; Colorado; On-site; USD 45,000 - 71,000; Part time

Job summary

The National Laboratory of the Rockies in Golden, CO invites a Graduate Internship focused on developing a digital twin for biorefinery processes. You will support engineers and data scientists in modeling biomass conversion, integrating sensor data, and building visualization dashboards to enable real-time monitoring and scenario analysis.

Preferred candidates are pursuing a master's or PhD in computational sciences or chemical engineering, with hands-on Python and/or C++, Cantera or CFD

Qualifications

• Currently pursuing or recently completed a master's degree in computational sciences, computational engineering, chemical engineering, biology, or related field.
• Experience programming in Python and/or C++.
• Experience modeling reactors and integrating with CFD frameworks.

Responsibilities

• 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.

Skills

Python

C++

Cantera

Digital twin

AI/ML

Education

Master's degree (computational sciences / engineering)

PhD student (or enrolled)

Tools

Cantera

CFD frameworks

Aspen Plus

Job description

The National Laboratory of the Rockies in Golden, CO invites a Graduate Internship focused on developing a digital twin for biorefinery processes. You will support engineers and data scientists in modeling biomass conversion, integrating sensor data, and building visualization dashboards to enable real-time monitoring and scenario analysis.

Preferred candidates are pursuing a master's or PhD in computational sciences or chemical engineering, with hands-on Python and/or C++, Cantera or CFD