Air quality prediction using Satellite data

The Atmospheric Science Data Center is at the forefront of utilizing cutting-edge missions to address environmental and climate challenges. Our team is passionate about making a positive impact on the world, and we are seeking talented individuals to join us in our mission to predict air quality pollutants through the application of neural networks and machine learning.

The data science intern will work closely with our experienced team to analyze geostationary satellite data (TEMPO) and develop predictive models using neural networks and machine learning algorithms. Your work will contribute directly to our efforts in understanding and forecasting air quality and pollution variables like NO2, HCHO and O3.

You will explore and implement neural network techniques to develop a Jupyter Notebook to predict air quality and pollution variables using TEMPO data.

Requirements:

- Pursuing a degree in Environmental Science, Engineering, Data Science or related field.
- Good understanding of environmental science concepts related to air quality.
- Strong programming skills in Python and familiarity with AI/ML frameworks like TensorFlow or PyTorch.

**Eligible Applicants:** All
**Academic Level:** Undergraduate Freshman, Undergraduate Sophomore, Undergraduate Junior, Undergraduate Senior, Graduate Master's, Graduate Doctoral, Post Doctoral
**Activity Type:** In-person
**Citizenship:** US Citizen Only
**Type:** Internships