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The Department of Bioengineering- Reznikov Laboratory
Project title: Upsampling of low-resolution/large-volume 3D tomographic images using generative adversarial neural networks applied to biological anthropology, medical imaging and evolutionary biology.
The aim of this project is to design and validate an open-ended upsampling algorithm using 3D images of historical (ancient mummies), clinical (dental cone-beam X-ray computed tomography) and basic science (bird eggshell) samples. Unique to this project is the availability of original multi-scale 3D image sets that span multiple resolution/volume scales, and which can be accurately superimposed (registered) in 3D, and can be expertly segmented (having meaningful features identified and assigned to the target class on a voxel basis). By applying GAN-CNN to a low-resolution/large-volume image using a high-resolution/low-volume image as ground truth, we will achieve 3D image upsampling ×n . To circumvent the inevitable increase of the data size (×n 3) we will implement a parallel segmentation algorithm that reduces voxel depth, because the ultimate objective is the segmentation of upsampled images.
- PhD degree in Computer Science or related disciplines
- Knowledge in computer vision-related topics including but not limited to 3D image segmentation
- In-depth knowledge of modern deep learning techniques for image processing
- Familiarity with one of the deep learning frameworks (Tensorflow, Pytorch)
- Understanding of micro-computed tomography is advantageous
- Strong Python, Matlab, and C++ experience
- Strong presentation, communication and writing skills
(AMURE Post-Doctoral Fellow Researcher) $34,611.00
Hours per Week:
35 (Full time)
McConnell Eng. Bldg.
Position Start Date:
Position End Date:
Deadline to Apply:
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