Postdoctoral Positions on Infant Brain Segmentation, Registration and Atlas Construction
Several postdoctoral positions are available in IDEA lab (https://www.med.unc.edu/bric/ideagroup), UNC-Chapel Hill, NC.
Segmentation: 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 deformable surface, level sets, and graph cut is highly desirable. People with machine learning background on image features and shape statistics 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 segmentation methods for infant brain segmentation and surface reconstruction.
Registration: The successful candidate should have a strong background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on feature learning and correspondence detection. Experience on medical image registration is highly desirable. People with experience on pairwise, group-wise and/or 4D registration are particularly encouraged to apply. Knowledge on brain development and also strong background on programming (good command of LINUX, C and C++, scripting, and Matlab) are desirable. The research topic will be the development and validation of 3D, 4D, and group-wise image registration methods for early brain development study.
Atlas Construction: Candidates with experience on patch-based sparse representation are encouraged to apply. The research topic will be the development of atlas construction methods for infant brain images.
The successful candidates will be part of a diverse group including radiologists, psychologists, physicists, biostatistician, and computer scientists, and will build upon the group's previous work on medical image analysis. If interested, please email resume to Dr. Dinggang Shen (dgshen@med.unc.edu).