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Postdoctoral Fellowship

We are hiring: Postdoc Position in Multicontrast MRI Analysis and Machine Learning in Big Data Analysis

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

Overview:

The primary project may include the development of techniques for prediction of cochlear surgical outcome of auditory function in patients with postlingual deafness using machine-learning and deep learning algorithms applied to BIG DATA combining imaging-features with psychological/behavioral parameters and 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 image processing and image analysis techniques for multivariate analysis of various imaging-features that are extracted on human brain MRI. One possible study that the selected postdoc fellow may participate in is to advance part of the present pipeline for the analysis of diffusion tensor imaging (DTI) or resting-state fMRI analysis (rs-fMRI), to predict the clinical outcome using graph-convolutional neural networks (Graph-CNN) and structural / functional connectivity data.

Required Qualifications:

Position qualifications include a Ph.D. in neuroscience, biomedical engineering, computer science or a related field. The successful applicant will have expertise in anatomical MRI, DTI or rs-fMRI analysis, strong skills in imaging processing such as image processing for DTI or rs-fMRI, statistical methods such as statistical parametric modeling, voxel-based / deformation-based morphometry and/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 learning (DNN, CNN) / various classification 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 (scikit-learn, Theano, PyMVPA), and Matlab is desirable. Salary and benefits are competitive.

Candidates should submit a CV (and cover letter and concise description of research interests & career goals if possible but not necessary) to Dr. Hosung Kim (hosung.kim@loni.usc.edu).

For further information, applicants should contact:

Hosung Kim, Ph.D. Assistant Professor, Laboratory of Neuro Imaging (LONI)

Email: hosung.kim@loni.usc.edu