JOB DESCRPTION

Position Title: Undergraduate Research Intern 

Location: US/Remote

FLSA Status: Non-Exempt/hourly- Full time, 40 hours/week

Duration: 10 weeks, early June – early August 2026 (start and end date to be determined)

Hourly pay range: $20.00

Apply here: 

Position Description

Are you an undergraduate interested in climate science, machine learning, data science, or polar research? Join the SETI Institute for a hands-on 10-week summer research experience contributing to an active National Science Foundation (NSF) project focused on improving predictions of Arctic summer sea ice cover. 

This NSF-Funded Project: Advancing Predictive Understanding of Summertime Arctic Sea Ice, develops advanced machine learning models to forecast summertime Arctic sea ice extent and regional coverage from a few weeks to an entire season ahead. It explores how sea ice preconditioning, ice-ocean-atmosphere interactions, and remote global climate influences (such as ocean temperatures and wind patterns) combine in complex, non-linear ways. Your work will help quantify the predictability horizon of these forecasts and better understand the physical drivers behind rapid Arctic changes — with real-world implications for global climate, economies, and

ecosystems.

This project is carried out in collaboration with researchers at UCLA and UCSB, creating an interdisciplinary framework that connects machine learning, climate dynamics, and Arctic prediction. This is an excellent opportunity for undergraduates to:

• Gain real research experience on a timely NSF-funded project addressing one of the most visible signals of climate change: the rapid loss of Arctic summer sea ice. 
• Build skills in advanced machine learning, big data analysis, and climate modeling — highly valued in academia, government (NOAA, NASA), and industry. 
• Work in a collaborative environment at the SETI Institute’s Carl Sagan Center while contributing to broader polar science goals. 

Responsibilities

• Analyze large observational datasets (satellite, reanalysis, and global climate records) and help develop or test ML models that capture non-linear interactions across local and remote drivers. 
• Contribute to interdisciplinary team efforts combining climate dynamics, data science, and predictive modeling. 
• Work directly with the project research team on machine learning applications for Arctic sea ice prediction.

 Qualifications:

• No prior Arctic research experience is required — enthusiasm for data-driven science and learning new computational tools is what matters most.

• Must be a current undergraduate (rising sophomores to seniors) majoring in any of the following disciplines: physics, atmospheric/climate science, computer science, data science, mathematics, engineering, or related fields. 
• Must have strong interest in machine learning. Python/data analysis, or climate modeling is highly desirable. 
• U.S. citizens or permanent residents per NSF guidelines.

Physical Requirements

• Frequent to continuous sitting
• Frequent to continuous use of computer monitor, mouse, and keyboard
• Frequent use of standard office equipment
• Occasional bending, reaching, kneeling

EEO/Veteran