Announcements of Opportunity
SURF: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF 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!
Remember: This is just one way that you can go about identifying a suitable project and/or mentor. Click here for more tips on finding a mentor. Announcements for external summer programs are listed here.
*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.
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Project: | Data-Driven Computing of Ultrasonic Brain Stimulation | ||||||||
Disciplines: | Applied and Computational Mathematics, Computer Science, Mechanical Engineering | ||||||||
Mentor: |
Hossein Salahshoor,
Assistant Professor, (EAS),
hossein.salahshoor@duke.edu, |
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Mentor URL: | https://salahshoor.pratt.duke.edu (opens in new window) | ||||||||
Background: |
NOTE: This project is being offered by a Caltech alum and is open only to Caltech students. The project will be conducted at Duke University in Durham, North Carolina. Ultrasound brain stimulation is an emerging technology in neuroscience. Computational modeling of ultrasonic waves in human brain is essential for guiding and optimizing these stimulation procedures. We aim to develop a data-driven computational model for predicting ultrasound wave patterns in human brain. |
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Description: | Using publicly available brain data, the student will develop a computational model that captures the intricate geoemtry of brain. Then we will solve the wave PDE in human brain, modeling the ultrasound induced deformation patterns. | ||||||||
References: | https://salahshoor.pratt.duke.edu | ||||||||
Student Requirements: | Numerical methods for differential equations, optimization, machine learning. Not required, but a great plus, is a working knowledge of finite element method | ||||||||
Programs: |
This AO can be done under the following programs:
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