Biomass Computational Science Intern - Summer 2027

Idaho National Laboratory•Idaho Falls, ID

1d•Onsite

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.
Identify engineering solutions that enhance reliability, throughput, and energy efficiency.
Scientific software development, data processing and visualization, physics-based simulation, machine learning, or analysis of scientific images and experimental data.
Improve understanding of how feedstock properties affect material behavior, preprocessing performance, and product quality.
Identify engineering solutions that improve process reliability, throughput, and energy efficiency.