Biomass Computational Science Intern - Summer 2027

Idaho National Laboratory is seeking a motivated intern to support our Physical Process Science and Realization team. As an intern you will support the development of computational tools and digital models for biomass feedstock preprocessing and handling systems. You will also collaborate with INL scientists and engineers to improve understanding of how feedstock properties influence material behavior, processing performance, and product quality, while helping identify engineering solutions that enhance reliability, throughput, and energy efficiency.

Depending on the student’s background and interests, project activities may include scientific software development, data processing and visualization, physics-based simulation, machine learning, or analysis of scientific images and experimental data. This research will help improve understanding of how feedstock properties affect material behavior, preprocessing performance, and product quality while identifying engineering solutions that improve process reliability, throughput, and energy efficiency. Students are not expected to have experience in all the areas listed below. Project assignments will be matched to each student’s technical background, skills, and professional interests. 

This team works a 9x80 schedule onsite at our Idaho Falls campus. Start date and duration of internship vary based on project and student's availability. 

Minimum Qualifications:

• Enrolled full time in an Undergraduate Degree Program, or Graduate Degree Program majoring in computer science, software engineering, data science, mechanical engineering, civil engineering, chemical engineering, electrical and computer engineering, applied mathematics, or a closely related field
• Experience operating EFAS or similar sintering systems
• A minimum overall 3.0 GPA
• Authorization to work in the U.S. (including CPT and OPT)
• Current Resume or CV
• Unofficial Transcripts (include current and completed degree programs)
• Current class schedule and number of credits

Preferred Qualifications:

• Experience developing, testing, documenting, and maintaining computational research tools using Python or similar programming languages; familiarity with scientific computing, data processing, visualization, software interfaces, workflow automation, version control, or high‑performance computing
• Experience applying or extending machine‑learning algorithms to experimental or computational datasets; familiarity with common ML libraries, GPU‑accelerated training, physics‑informed ML, or model deployment
• Experience using computational modeling tools for particle, fluid, or multiphysics simulations; familiarity with discrete element method (DEM), computational fluid dynamics (CFD), CAD tools, or mesh generation
• Experience developing or applying computational methods to process and analyze scientific images or other experimental datasets; experience improving image quality, identifying/classifying features, measuring structural characteristics, creating 2D/3D visualizations, or automating data‑analysis workflows

Compensation:

• Undergraduate Internships ($26.94/hour - $32.41/hour)
• Masters Internships ($35.34/hour - $37.73/hour)
• PhD Internships Pre-Candidacy ($36.83/hour - $39.33/hour)
• PhD Internships Post-Candidacy ($38.65/hour - $41.29/hour)
• Multi-Level: Internships are typically multi-level posting and you will be placed at the appropriate level dependent on degree field and level of education.

Physical Requirements:

While performing the duties of this classification, the employee is frequently required to stand, walk, sit, stoop, kneel, bend, use hands to handle materials, manipulate tools, type on a keyboard, reach with hands and arms, and operate job-related equipment. The employee must occasionally lift and/or move up to 35 pounds without assistance. Sufficient visual acuity and hearing capacity to perform the essential functions and interact with people is required. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.