Postdoctoral Research Associate
- University of Georgia
- Location: Athens, GA
- Job Number: 7069178
- Posting Date: Aug 26, 2020
- Application Deadline: Open Until Filled
Job DescriptionUse machine learning to model spatiotemporal dynamics of cilia and identify abnormalities.
We are looking to hire one researcher with a background in statistical machine learning, computer vision, and/or biomedical imaging. Strong skills in computer programming, statistics, and linear algebra are essential. Researchers with experience in bioinformatics, cellular biology, infectious diseases, or other computational biology backgrounds are encouraged to apply. Individuals from traditionally underrepresented groups in STEM are likewise especially encouraged to apply.
Join Dr. Shannon Quinn and his interdisciplinary research group at University of Georgia and help develop spatiotemporal models of ciliary motion in order to detect and ultimately help diagnose ciliopathies in humans. Cilia are microscopic hairlike structures that line the exteriors of cells in the throat, nose, lungs, kidneys, and brain. In humans, they beat in rhythmic patterns to clear particulates and pathogens, and when their regular motion is perturbed, numerous multi-organ pathologies result. While no objective method for identifying these perturbations exists, we have developed a proof of concept, drawing from dynamic textures in machine vision.
The three main efforts of the project are: (1) deriving a robust segmentation procedure to automatically identify regions of cilia in videos, (2) building a spatiotemporal model of ciliary motion dynamics to recognize type and extent of motion perturbations, and (3) deploying these algorithms in an open source web framework, CiliaWeb, for use by clinicians and domain researchers that incorporates feedback mechanisms into the model predictions.
Preferred candidates should also be proficient in Python and at least one of the many popular deep learning libraries (e.g., PyTorch, TensorFlow, Keras), familiar with the git versioning system, and willing to conduct their research according to the principles of Open Science: reproducibility, benchmarking, pre-registration, pre-publication, and open licensing. Successful candidates are also expected to be team players who can fill a leadership role in executing a research agenda, and lead by example as a mentor for predoctoral and undergraduate student researchers.