Announcements of Opportunity
Related Pages 
SURF@JPL: Announcements of Opportunity
Announcements of Opportunity are posted by JPL technical staff for the SURF@JPL program. Each AO indicates whether or not it is open to non-Caltech students. If an AO is NOT open to non-Caltech students, please DO NOT contact the mentor.
Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions!
**Students applying for JPL projects should complete a SURF@JPL application instead of a "regular" SURF application.
**Students pursuing opportunities at JPL must be U.S. citizens or U.S. permanent residents.
<< Prev
Record
39 of
40
Next >>
Back To List
| Project: |
Climate model simulations to support weather forecasting using machine learning
(JPL AO No. 13990)
|
||||||||
| Disciplines: | Computer Science, Deep Learning / Machine Learning | ||||||||
| Mentor: |
Alphan Altinok,
(JPL),
Alphan.Altinok@jpl.nasa.gov, |
||||||||
| Background: | Precipitation forecasting is a key decision support application for many agencies. Dynamical methods compute climate models starting from some initial conditions, while empirical models aim to leverage existing observational data to train machine learning algorithms. For climate models aiming to capture atmospheric processes, manual calibration often results in high uncertainties due to the probabilistic computations involved. This project aims to test generative machine learning models as an alternative manual calibration of forward models. | ||||||||
| Description: | This project will focus on development and optimization of generative-adversarial networks (GAN) to learn the evolution of climate models. The student intern will become familiar with atmospheric data and learn optimization methods, developing, implementing, and testing GAN architectures on GPU clusters. | ||||||||
| Student Requirements: | Required: Strong background deep learning concepts and optimization, GAN architectures, metrics, and training failure modes. Familiarity with atmospheric data and related file structures, as well as remote/cloud based computing infrastructure. Fluency with Python, SKLearn, TensorFlow and/or PyTorch, Docker, Anaconda. Strong communication and writing skills. | ||||||||
| Location / Safety: | Project building and/or room locations: . Student will need special safety training: . | ||||||||
| Programs: |
This AO can be done under the following programs:
|
<< Prev Record 39 of 40 Next >> Back To List