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Radar Intern

Job in Norman, Cleveland County, Oklahoma, 73019, USA
Listing for: Rainmaker Technology Corporation
Apprenticeship/Internship position
Listed on 2026-05-16
Job specializations:
  • Research/Development
    Data Scientist
  • Engineering
Salary/Wage Range or Industry Benchmark: 60000 - 80000 USD Yearly USD 60000.00 80000.00 YEAR
Job Description & How to Apply Below

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 Tensor Flow
  • Experience with data analysis tools such as Py-ART, Met Py, 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
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