Biomass Computational Science Intern - Summer 2027
About The Position
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.
Requirements
- 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
Nice To Haves
- 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
Responsibilities
- Support the development of computational tools and digital models for biomass feedstock preprocessing and handling systems.
- Collaborate with INL scientists and engineers to improve understanding of how feedstock properties influence material behavior, processing performance, and product quality.
- Help identify engineering solutions that enhance reliability, throughput, and energy efficiency.
- Engage in activities such as scientific software development, data processing and visualization, physics-based simulation, machine learning, or analysis of scientific images and experimental data.
Benefits
- Paid holiday time off
- Travel reimbursement may be available for eligible interns
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Associate degree
