Remote ML Intern: Climate Data Analytics

North Carolina State University; Raleigh (NC); Remote; USD 20,664,000 - 27,552,000; Full time

Benefits offered by this job

Health insurance for temporary workers

LEAD courses for career development

Free attendance to non-revenue sports

Job summary

The North Carolina Institute for Climate Studies (NCICS) at NC State University in Asheville, NC is seeking two temporary student interns to work remotely on a project applying machine learning to environmental observations and satellite data. Interns will collaborate with NCICS scientists and NOAA partners to learn advanced ML models and data analytics techniques.

Typical duties include processing and analyzing environmental data, learning new tools, and contributing to a team effort to

Qualifications

• High school diploma, or equivalent, and college coursework pursuing an undergraduate or graduate degree in computer science, statistics, environmental science, mathematics, engineering, meteorology, atmospheric science, or related field.
• Direct experience with applied machine learning and/or large language models (LLM).
• Experience performing statistical analyses.
• Experience working with environmental data.

Responsibilities

• Apply machine learning techniques to environmental observations and satellite data.
• Collaborate with NC Institute for Climate Studies scientists and NOAA collaborators.
• Learn state-of-the-art ML models and analytics tools as part of the internship.

Skills

Applied ML

LLM

Statistical analyses

Environmental data

Communication

Multitasking

Independence

Education

BS in CS/Statistics/Env Sci

Job description

The North Carolina Institute for Climate Studies (NCICS) at NC State University in Asheville, NC is seeking two temporary student interns to work remotely on a project applying machine learning to environmental observations and satellite data. Interns will collaborate with NCICS scientists and NOAA partners to learn advanced ML models and data analytics techniques.

Typical duties include processing and analyzing environmental data, learning new tools, and contributing to a team effort to