Job Opportunity

Baby Connectome Project - Postdocs

Position
Several postdoctoral research associate positions are immediately available at the Multimodal Imaging in Neuro Disorders (MIND) laboratory of the University of North Carolina at Chapel Hill. The successful candidates will focus on developing novel infant-centric MRI tools that will be critical for gaining insights into the working mechanisms of the human brain in the first years of life.

Candidate Requirements
We are seeking highly motivated individuals who have demonstrated academic excellence, including publications in first-class journals and conferences. The candidate should have a Ph.D. (or equivalent) in Computer Science, Applied Mathematics/Statistics, Electrical Engineering, Biomedical Engineering, or related fields. Experience in diffusion MRI, compressed sensing, machine learning, and numerical optimization is highly desirable. Competence in programming beyond MATLAB (good command of Linux, C/C++, CMake, ITK, VTK, etc.) is essential. The candidate should ideally have a strong background in MR data processing, analysis, and reconstruction.

Research Environment
The successful candidate will be part of a diverse group including radiologists, psychologists, physicists, biostatisticians, and computer scientists, and will build upon the group's extensive foundation on medical image analysis. The candidate will also have the opportunity to work with state-of-the-art equipment such as a PET-MR scanner and a 7T whole-body MRI scanner.

Contact
The application should include a cover letter indicating research interest areas, curriculum vitae, and the contact information of three references. For more information or to submit your application, please contact Dr. Pew-Thian Yap (ptyap@med.unc.edu).

Deadline
Positions open until filled.

Postdoctoral Positions on Learning-based Large-Scale Medical Image Segmentation and Registration

Postdoctoral positions are available in IDEA lab (https://www.med.unc.edu/bric/ideagroup), UNC-Chapel Hill, NC.

Learning-based Image 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 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 brain images, such as infant brain images from our recently awarded Baby Connectome Project (BCP) as well as the elderly brain images from Alzheimer's Disease Neuroimaging Initiative (ADNI).

Large-Scale Image Registration: The successful candidate should have a strong background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on landmark detection, feature learning, and large-scale dataset image registration. Experience on medical image registration is highly desirable. People with experience on pairwise, group-wise and/or longitudinal image registration for large population dataset are particularly encouraged to apply. Knowledge on brain development/aging, machine learning, and deep learning are desirable. The research topic will be the development and validation of learning-based 3D, 4D, and group-wise image registration methods for early brain development or aging studies.

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).

Postdoctoral Position on Brain Network Analysis

A postdoctoral position on brain network analysis, with application to early brain development (using the data acquired from our Baby Connectome Project (BCP)) and early diagnosis of brain disorders (including Alzheimer’s Disease), is available in IDEA lab (https://www.med.unc.edu/bric/ideagroup), UNC-Chapel Hill, NC.

The successful candidate should have a strong background on functional/structural MRI processing and also the interpretation of neuroscience questions based on the imaging analysis results. Experiences on processing of resting-state fMRI and DTI, development of novel brain network construction and comparison methods, and interpretation of disease-related functional and structural brain alterations, are highly desirable. People with experience on large dataset analysis are particularly encouraged to apply. Strong knowledge on programming (good command of LINUX, C and C++, scripting, and Matlab) is also expected. The research topic will be the development and validation of novel learning-based brain network analysis methods, or the application of existing tools to large dataset for early brain development study or early diagnosis of brain disorders.

The successful candidates will be part of a diverse group including radiologists, psychologists, neuroscientists, physicists, biostatisticians, and computer scientists, and will build upon the group's previous work on neuroimaging analysis. If interested, please email resume to Dr. Dinggang Shen (dgshen@med.unc.edu).

Postdoctoral Position on Pelvic/Prostate Segmentation

A postdoctoral position on segmentation of pelvic structures (including prostate) from planning and daily treatment CT images is available in IDEA lab (https://www.med.unc.edu/bric/ideagroup), UNC-Chapel Hill, NC.

The successful candidate should have a strong background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on deep learning, shape representation, and shape statistics. 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) are desirable. The research topic will be the development and validation of learning-based segmentation methods for extracting pelvic structures from planning and daily treatment CT 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). 



Postdoctoral Position on Imaging Genomics

One postdoctoral position is available in IDEA lab (https://www.med.unc.edu/bric/ideagroup), UNC-Chapel Hill, NC.

Imaging Genomics: The successful candidate should have a strong background on Biomedical Engineering, Electronic Engineering, Computer Science, or relatedly majors, preferably with emphasis on neuroimaging analysis and genomics. Experience on brain disease diagnosis is highly desirable. People with machine learning background on feature representation and regression are particularly encouraged to apply. Strong knowledge on programming (good command of LINUX, C/C++, Python, Matlab, etc.) is desirable. The research topic will be the development and validation of innovative methods for imaging genomics.

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). 

 


Postdoctoral Position on Medical Image Indexing

One postdoctoral position is available in IDEA lab (http://bric.unc.edu/ideagroup), UNC-Chapel Hill, NC.

Image Indexing: The successful candidate should have a strong background on Biomedical Engineering, Electronic Engineering, Computer Science, or relatedly majors, preferably with emphasis on image processing and analysis. Experience on image indexing and retrieval is highly desirable. People with machine learning background on image features and image similarity measurement are particularly encouraged to apply. Strong knowledge on programming (good command of LINUX, C/C++, Python, Matlab, etc.) is desirable. The research topic will be the development and validation of image indexing methods for medical applications.

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).


PhD Positions in UNC-Chapel Hill

One PhD position is available, for each of the following research directions, in the IDEA lab of UNC-Chapel Hill, NC (http://bric.unc.edu/ideagroup).

Brain Image Segmentation and Surface Labeling: The successful candidate should have a strong background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on image processing and pattern recognition. Experience on medical image segmentation and analysis is highly desirable. People with machine learning background are particularly encouraged to apply. Knowledge on neuroscience and programming background (good command of LINUX, C and C++, scripting, and Matlab) are desirable. The research topic will be the development and validation of methods for atlas-based tissue segmentation (of neonatal brain images) and cortical surface labeling of brain images.

Deformable Segmentation: The successful candidate should have a strong background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on image feature extraction, shape representation, and shape statistics. 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) are desirable. The research topic will be the development and validation of statistical deformable segmentation methods for lung, liver, prostate, and brain.

Neuroimage classification: The successful candidate should have a strong background on Electronic Engineering, Biomedical Engineering, Statistics, or Computer Science, preferably with emphasis on machine learning, pattern classification, multivariate image analysis, or computer vision. Experience on neuroimage analysis is highly desirable. People with machine learning background are particularly encouraged to apply.

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).


Postdoctoral Position on Infant Brain Segmentation, Registration and Atlas Construction

Several postdoctoral positions are available in IDEA lab (http://bric.unc.edu/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).


Postdoctoral Position on PET/MRI Data Analysis

A postdoctoral position on PET/MRI data analysis is available in IDEA lab (http://bric.unc.edu/ideagroup), UNC-Chapel Hill, NC.

The successful candidate should have a strong background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on sparse learning, super-resolution, and data fusion. Experience on image feature learning, selection, integration and prediction is highly desirable. People with machine learning background on sparse representation and regression are particularly encouraged to apply. Strong knowledge on programming (good command of LINUX, C and C++, scripting, and Matlab) are desirable. The research topic will be the development and validation of image processing methods for integrating PET and MRI for data enhancement and resolution improvement.

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).

 


Postdoctoral Position on Machine Learning

A postdoctoral position on machine learning with application to neuroimage-based brain disease diagnosis and prediction is available in UNC-Chapel Hill. The successful candidate should have a strong background on Electronic Engineering, Biomedical Engineering, Statistics, or Computer Science, preferably with emphasis on machine learning, pattern classification, regression methods, or sparse representation. People with strong experience on machine learning are particularly encouraged to apply.

The successful candidate 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. The research topic will be the development and validation of novel machine learning methods for early diagnosis and prediction of brain disorders. Please visit group website (http://bric.unc.edu/ideagroup) for more information.

If interested, please email resume to Dr. Dinggang Shen (dgshen@med.unc.edu).


 

Postdoctoral Positions on Image Segmentation and Registration

Several postdoctoral positions are available in IDEA lab (http://bric.unc.edu/ideagroup), UNC-Chapel Hill, NC.

Position 1 (Deformable Segmentation): The successful candidate should have a strong background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on image feature extraction, shape representation, and shape statistics. 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) are desirable. The research topic will be the development and validation of statistical deformable segmentation methods for prostate and brain.

 

Position 2 (Brain Image Registration): The successful candidate should have a strong background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on image analysis, or computer vision. Experience on medical image registration and analysis is highly desirable. People with group-wise registration or 4D registration are particularly encouraged to apply. Knowledge on neuroscience and programming background (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 brain image analysis.

 

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).


Postdoctoral Position on Prostate Segmentation

A postdoctoral position on segmentation of prostate from daily CT images is available in IDEA lab (http://bric.unc.edu/ideagroup), UNC-Chapel Hill, NC.

The successful candidate should have a strong background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on image feature extraction, shape representation, and shape statistics. 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) are desirable. The research topic will be the development and validation of statistical deformable segmentation methods for segmenting prostate from daily treatment CT 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).


Postdoctoral Position in 4D Image Registration and Segmentation

A postdoctoral position on 4D image registration and segmentation is available in IDEA lab (http://bric.unc.edu/ideagroup), UNC-Chapel Hill, NC.The successful candidate should have a strong background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on image analysis, or computer vision. Experience on medical image registration and segmentation is highly desirable. People with machine learning background are particularly encouraged to apply. Knowledge on neuroscience and programming background (good command of LINUX, C and C++, scripting, and Matlab) are desirable. The research topic will be the development and validation of 4D image registration and segmentation methods for longitudinal image analysis.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).


Postdoctoral Research Associate in Diffusion MRI

A postdoctoral research associate in diffusion MRI is available at the Multimodal Imaging in Neuro Disorders (MIND) laboratory of the University of North Carolina at Chapel Hill. Our current effort is to enhance understanding of the working mechanism of the brain via diffusion MRI by automatically mining information latent in images for the purposes of growth and disease analyses. The successful candidate will support our efforts in advancing novel MR technologies for neuroscience applications. We are seeking highly motivated individuals who have demonstrated academic excellence, including publications in first-class journals and conferences. The candidate should have a Ph.D. (or equivalent) in Computer Science, Applied Mathematics/Statistics, Electrical Engineering, Biomedical Engineering, or related fields. Experience in diffusion MR physics/acquisition, compressed sensing, and optimization is highly desirable. Competence in programming beyond MATLAB (good command of Linux, C/C++, CMake, ITK, VTK, etc.) is essential. The successful candidate will be part of a diverse group including radiologists, psychologists, physicists, biostatistician, and computer scientists, and will build upon the group's extensive foundation on medical image analysis. The candidate will also have the opportunity to work with state-of-the-art equipment such as a PET-MR scanner and a 7T whole-body MRI scanner. Applications should include a cover letter indicating the research area of interest, a curriculum vitae, and the contact information of three references. For more information or to submit your application, please contact Dr. Pew-Thian Yap (ptyap@med.unc.edu).


Graduate Research Assistant (GRA)

Graduate research assistant (GRA) 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 extraction, shape representation, and shape statistics. 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) are desirable. The research topic will be the development and validation of deformable segmentation and registration methods for serial CT images of prostate acquired during the radiotherapy of prostate cancer.

Registration: The successful candidate should have a strong background on Electrical or Biomedical Engineering, or Computer Science, preferably with emphasis on image matching. Experience on medical image registration is highly desirable. People with machine learning background are particularly encouraged to apply. Strong knowledge on neuroscience and programming (good command of LINUX, C and C++, scripting, and Matlab) are desirable. The research topic will be the development and validation of various methods for deformable registration of a set of images, or a set of serial (4D) images from different subjects.

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).