SURF: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program. Additional AOs for the Amgen Scholars program can be found here.
Specific GROWTH projects being offerred for summer 2018 can be found here.
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.
Announcements for external summer programs are listed here.
Students pursuing opportunities at JPL must be
U.S. citizens or U.S. permanent residents.
|Project:||Deep Cell - Deep Learning for Single-Cell Image Segmentation|
|Disciplines:||Computer Science, Biology|
David Van Valen,
Assistant Professor, (BBE),
|Mentor URL:||http://www.vanvalen.caltech.edu (opens in new window)|
|Background:||Numerous experiments in biology require the accurate identification of individual cells from microscope images. While this image segmentation problem has been challenging for classical computer vision approaches, it is a natural fit for modern deep learning algorithms. The goal of this project is to put together a deep learning based computer vision system that can reliably identify single cells from 2D and 3D data.|
– Code existing deep learning architectures and generate novel deep learning architectures for image segmentation and object tracking.
– Assist with crowd sourcing of data set annotation.
– Test and document the performance of different deep learning architectures.
– Create interactive image analysis notebooks with Jupyter.
– Interface our deep learning based image segmentation pipeline with existing GUIs.
Deep learning for computer vision
Deep learning with python
Experience with MATLAB, Python, tensorflow, cloud computing (AWS/Google cloud engine), Jupyter, and Python web development (Django/Flask) is great, but not required (plenty of opportunity for on the job training!)
This AO can be done under the following programs:
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