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Postdoctoral Scholar-Research AssociateApply Administration Los Angeles, California
The University of Southern California (USC), founded in 1880, is located in the heart of downtown Los Angeles and is the largest private employer in the City of Los Angeles. USC is consistently ranked among the nation’s most prestigious universities, and the USC Leonard Davis School of Gerontology features one the world’s best degree and research programs in gerontology. The USC Davis School has an international reputation as a hub of aging research and, with additional strong programs throughout the university, leads the way in defining and advancing the field of gerontology.
One of our Labs at the USC Leonard Davis School of Gerontology is dedicated to studying the effects of traumatic brain injury upon the aging brain using multimodal neuroimaging (MRI, fMRI, DTI, DSI, MRS, MRA, PET, CT), neuroelectrophysiology (EEG, MEG) and computational modeling grounded on biophysics and applied mathematics. Approaches afforded by fields as varied as network theory, machine learning, neural networks & deep learning, multivariate statistics, scientific visualization and nonlinear dynamics have allowed the PI and his collaborators to quantify brain disease evolution in older adults and to contribute to the development of novel, patient-tailored approaches to clinical patient care.
The laboratory seeks a postdoctoral research scholar interested in using neuroimaging, big data science and related approaches to study how traumatic brain injury and intracerebral hemorrhage impact neurodegeneration, neurocognitive function and connectome reorganization in older adults. The scholar will use advanced techniques for visualization, computational neuroanatomy, longitudinal neuroimage analysis and multivariate statistical models of brain aging to facilitate scientific discovery and to assist the development of next-generation protocols for patient-tailored clinical care and personalized medicine. The ideal candidate will have expertise in multimodal structural MRI and diffusion tensor imaging (DTI), strong skills in image processing (particularly registration, segmentation, surface modeling, voxel-based morphometry), network theory for connectomic analysis, experience with neuroimaging analysis (FreeSurfer, AFNI, FSL, SPM or similar) and statistical analysis (SPM, SPSS). Expertise in machine learning, including neural networks, deep learning and classification methods (support vector machines, probabilistic graphical models, ensemble models, etc.) would be highly beneficial. Experience in nonlinear dynamics or neurophysiology [electro- or magnetoencephalography (EEG, MEG)] is welcome. Excellent scientific writing skills and a strong publication record are highly desirable. Outstanding programming skills (MATLAB, preferably) and working knowledge of Linux are required. The successful applicant will be able to work independently with a small amount of supervision and should demonstrate good interpersonal skills as well as an interest in collaborative research.
A PhD or equivalent doctorate in neuroscience, neurobiology, engineering, biophysics, computer science or applied mathematics is required. Applicants with degrees in related fields will also be considered, and individuals with strong quantitative and computational backgrounds are particularly encouraged to apply. A strong interest in building an outstanding publication record is essential.
Minimum Education: Ph.D. or equivalent doctorate within previous five years
Minimum Experience: 0-1 year
Minimum Field of Expertise: Directly related education in research specialization with advanced knowledge of equipment, procedures and analysis methods.
REQ20041912 Posted Date: 04/03/2017 Apply