Postdoctoral Fellow Position in Deep Learning

LONI, University of Southern California
Los Angeles, CA USAv
Job Type: 
Full Time
Closing Date: 
Saturday, December 1, 2018

Postdoctoral Fellow Position in Machine Learning for Analysis of Stroke and Big Neuroimaging Data

Laboratory of Neuro Imaging (LONI), Neuroimaging and Informatics Institute

Duration: 2 years (option to renew for additional years)
Start date: Soon as possible (start date is negotiable)
Salary: Depends on experience, in accordance with NIH guidelines


The primary project may include the development of a clinical scoring system for assessing the status of patients with acute stroke using machine learning (ML) and deep-learning (DL) algorithms applied to BIG DATA combining neuroimaging-features with clinical parameters. The laboratory provides ample opportunity for the development of innovative, focused research and a broad collaborative clinical neuroscience experience as well as for numerous publications in high impact journals. The other research focuses on developing ML/DL-based techniques for neuroimaging data analysis to advance understanding of brain aging in healthy population as well as patients with neurodegeneration. One possible study that the selected postdoc fellow may participate in is to predict brain age in healthy adults and brain age acceleration in AD or other dementia applying DL on multicontrast structural / functional MRI.

Required Qualifications:

Position qualifications include a Ph.D. in electrical engineering, computer science, biomedical engineering, neuroscience or a related field. The successful applicant will have expertise in CT, CTA, anatomical MRI, DWI, DTI or rs-fMRI analysis, strong skills in imaging processing such as registration, segmentation and/or surface modeling, voxel-wise statistical methods such as statistical parametric modeling, or graph theory for the structural / functional connectivity analysis. Experience with neuroimaging analysis programs (AFNI, FSL, SPM, FreeSurfer or other relevant programs), and statistical analysis (MATLAB & toolbox – SPM, SurfStat, R) are also required. A person with expertise in machine learning approaches such as deep convolutional neural network (CNN) / various classification /regression methods (SVM, probabilistic graphical models, ensemble models) would be highly encouraged, even without broad neuroscience experience. Excellent scientific writing skills and strong publication records are highly desired. Solid big data programming skills with a working knowledge of Linux, C/C++, Python, deep-learning packages/libraries (TensorFlow, NiftyNet, Caffe, etc), and Matlab is desirable. Salary and benefits are competitive.

Candidates should submit CV and (cover letter and concise description of research interests & career goals if possible but not necessary) to Dr. Hosung Kim (

For further information, applicants should contact:
Hosung Kim, Ph.D. Assistant Professor of Neurology, Laboratory of Neuro Imaging (LONI) Email:
Danny JJ Wang, Ph.D. Professor of Neurology and Radiology; Email: