Diagnostic Radiology, Magnetic Resonance Imaging, Research & Statistical Methods
Dr. Lili He’s artificial intelligence for computer aided diagnosis lab (https://aicad.research.cchmc.org) is committed to lending the group’s interdisciplinary expertise in medical imaging, computer science and biomedical engineering to facilitate major breakthroughs in the medical field by optimizing imaging acquisition and aiding doctors in disease diagnosis, outcome prediction, image segmentation and interpretation, as well as, treatment decision making and assessment. We are now looking for highly motivated candidates for Post-doctoral Research Fellows or Research Associates and will also consider Imaging Analysts and Software Engineers. The research projects include: 1. Develop structural, functional and diffusion MRI prognostic biomarkers and machine learning models of early detection/prediction of neurodevelopmental deficits and other important clinical outcomes for high risk newborns and infants; 2. Develop machine learning/deep learning methods using conventional MRI and MR elastography data to accurately detect and quantify liver fibrosis, using biopsy-derived histologic data as the reference standard; 3. Large-scale collaborative analyses of radiomics and genomics data for prediction/diagnosis neurodevelopmental disorder or liver, bowel other disease prediction; and 4. Develop machine learning/deep learning algorithms for MRI image reconstruction.
SCOPE: Scientific research, Image processing, Data analysis
PhD degree, or equivalent doctoral degree, in computer science, mathematics, biomedical engineering, bioinformatics, electrical engineering, physics or related field.
Strong programming skills with Python, Matlab.
Strong communication skills in written and verbal English. Trackable publication records.
Experience in machine learning and deep learning development with Scikit-learn, Deep learning package (e.g., Tensorflow, Keras), and Matlab packages.
MRI image research experience or experience in analysis of high throughput sequencing genomics data (ChIP-Seq, DNase-Seq, and/or ATAC-Seq) is a plus.
Additional Salary Information: Salary is based on NIH Scale and experience
Internal Number: 117314
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