Postdoctoral and visiting student/scholar positions
— Fetal/infant brain MRI processing for early diagnosis of autism
Postdoctoral research associate and visiting student/scholar positions are available in the Department of Radiology at the University of North Carolina at Chapel Hill (UNC-Chapel Hill), for the development of fetal/infant brain MRI processing algorithms, including tissue segmentation, parcellation, and early diagnosis of autism.
- The successful candidate should have a background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on medical image analysis.
- Experience on medical image segmentation using deep learning is highly desirable.
- Participants with good performance in our organized infant segmentation challenges iSeg-2017 and iSeg-2019 are particularly encouraged to apply.
Dr. Li Wang’s Developing Brain Computing (DBC) lab is committed to developing innovative computational methods and tools for processing and analyzing medical imaging data, especially for fetal/infant brain MRI. We are focusing on fetal/infant brain processing since 2007. Currently, we have pioneered a comprehensive set of advanced fetal/infant-dedicated pipeline, iBEAT V2.0 Cloud (http://www.ibeat.cloud/), for skull stripping and tissue segmentation of fetal/infant brain MRIs. iBEAT V2.0 Cloud can handle fetal/infant brain images from multiple sites with various scanners and protocols. Up to date, we have successfully processed 5000+ infant brain images from 70+ institutions, including Boston Children’s Hospital/Harvard Medical School, Stanford University, Yale University, University of Maryland, University of California, University of Pennsylvania, Washington University in St. Louis, Tokyo Metropolitan University, Arkansas Children’s Research Institute, and Princeton University. We have received numerous praise and positive feedback from the community.Our computational tools and discoveries on infant brain development has been highlighted in the National Institute of Mental Health (NIMH)’s 2015-2020 Strategic Plan. The new positions are for the further development of fetal/infant brain MRI processing algorithms, including tissue segmentation, parcellation, and early diagnosis of autism.
If interested, please email your CV to Dr. Li Wang: email@example.com
Postdoctoral and visiting student/scholar positions — Early Brain Biomarker
Postdoctoral research associate and visiting student/scholar positions are available in the Department of Radiology and Biomedical Research Imaging Center (BRIC) at the University of North Carolina at Chapel Hill (UNC-Chapel Hill). Our current focuses are to identify early brain biomarkers on abnormal brain development.
The successful candidate should have a strong background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on image feature learning and segmentation. Experience on medical image segmentation using deep learning and shape statistics is highly desirable. People with machine learning background on medical imaging analysis are particularly encouraged to apply. Strong knowledge on programming (good command of LINUX, C and C++, scripting, and Matlab) is desirable. The research topic will be the development and validation of tissue segmentation and ROI labeling methods for infant brain images with risk of abnormal brain development, such as typical control subjects from our recently awarded Baby Connectome Project (BCP) as well as with-risk subjects from publicly dataset.
Software Engineer for Infant Brain Image Processing
A software engineer position is available in Biomedical Research Imaging Center (BRIC) at UNC-Chapel Hill (http://www.med.unc.edu/bric/), for further development of infant brain MR image processing pipeline (iBEAT, http://www.nitrc.org/projects/ibeat). The successful applicant will be trained to work broadly in medical image processing, and to help integrate new infant brain MRI processing algorithms developed in the research lab.
This position needs to work with a multidisciplinary team on infant brain MR image processing. The successful applicant must have a master degree (or above) in computer science or related engineering fields. He/She must be strong in programming and GUI design for cross-platform including Windows, Linux, and Mac OS X, and familiar with C/C++, Matlab, and ITK/VTK. Experience with Linux is highly desired. Knowledge on neuroimaging analysis, computer vision, pattern recognition, computer graphics, and image processing is very helpful. Knowledge of well-known software, such as FreeSurfer, FSL, SPM, and Connectome Workbench, is also very helpful.
If interested, please email resume to Dr. Li Wang: firstname.lastname@example.org.