Position: Summer Research Intern: Weather & Machine Learning

Duration: 10–12 weeks

Level: Undergraduate or Graduate

Overview: We are seeking a motivated student with coursework in meteorology and hands-on experience with machine learning to join our research team for the summer. The intern will contribute to a project focused on using machine learning to better understand and track microscale features within winter weather systems using radar data.

Responsibilities

• Work with radar datasets to identify and organize cases of microscale features
• Assist in preparing and processing data for use in machine learning models
• Help evaluate and visualize model results using Python-based tools
• Contribute to team meetings and discussions about storm behavior and model performance
• Document progress and assist in preparing summaries of findings

Qualifications

• Currently enrolled in an undergraduate or graduate program in meteorology, atmospheric science, or a related field
• Basic familiarity with radar products through coursework or experience
• Prior coursework or project experience in machine learning or data science
• Proficiency in Python for data analysis

Preferred

• Coursework or experience in radar meteorology
• Experience with or understanding of cloud seeding atmospheric effects
• Familiarity with deep learning frameworks such as PyTorch or TensorFlow
• Experience with data analysis tools such as Py-ART, MetPy, xarray, or similar
• Prior research experience of any kind (REU, class projects, lab work)

What You Will Gain

• Hands-on experience applying machine learning to real operational radar data
• Mentorship from researchers across meteorology and data science
• A meaningful research contribution suitable for graduate school applications
• Collaborative work environment bridging atmospheric science and modern data science methods