Graduate Digital Twin Developer Intern – Biorefinery

National Renewable Energy Laboratory; Golden (CO); On-site; USD 44,500 - 71,200; Full time

Benefits offered by this job

Medical, dental, and vision insurance

403(b) Employee Savings Plan

Flexible sick leave

Job summary

The National Renewable Energy Laboratory in Golden, Colorado is offering a Graduate Internship focused on Digital Twin Development for Biorefinery Processes. This full-time position aims to support engineers in creating a digital twin representation to enhance biofuel and bioproduct production.

The ideal candidate is a full-time student or recent graduate in computational sciences or related fields, skilled in Python or C++, and will gain experience in innovative energy systems and modeling.

Qualifications

• Minimum 3.0 cumulative GPA.
• Enrolled as a full-time student in a bachelor’s, master’s, PhD, or recent graduate program.
• Currently pursuing or recently completed a master’s or PhD in relevant fields.

Responsibilities

• Design and implement process models for biomass conversion and separation units.
• Integrate sensor and historical process data into digital twin architecture.
• Develop simulation tools and dashboards for visualization and analysis.
• Contribute to model validation using pilot-scale data.

Skills

Programming in Python

Modeling chemical reactors

Experience with CFD frameworks

Techno-economic modeling

Strong problem-solving skills

Communication skills

Collaboration skills

Education

Currently enrolled in a master's or PhD program

Minimum 3.0 cumulative GPA

Tools

Cantera

Aspen Plus

AnyLogic

TwinCAT

Siemens Xcelerator

Job description

The National Renewable Energy Laboratory in Golden, Colorado is offering a Graduate Internship focused on Digital Twin Development for Biorefinery Processes. This full-time position aims to support engineers in creating a digital twin representation to enhance biofuel and bioproduct production.

The ideal candidate is a full-time student or recent graduate in computational sciences or related fields, skilled in Python or C++, and will gain experience in innovative energy systems and modeling.