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Postdoctoral Scholar - Research Associate

USC Institute for Neuroimaging and Informatics Los Angeles, California

The USC Mark and Mary Stevens Neuroimaging and Informatics Institute and Laboratory of Neuro Imaging (INI, are world leaders in the development of advanced computational and scientific approaches for the comprehensive mapping of brain structure and function. LONI’s unique multidisciplinary environment and cutting edge resources allow for integration of clinical, psychological and genotypic information with neuroimaging phenotypes for research questions in neurology, psychiatry, and developmental neurobiology.

Dr. Duncan is an Assistant Professor of Neurology at LONI. Her work combines electrical engineering, mathematics, and neurology. She has focused on developing and applying novel mathematical tools, such as nonlinear factor analysis, to intracranial EEG data for applications such as the detection of seizure onset as well as resting state networks. She has also developed novel dimensionality reduction techniques to study Alzheimer’s disease using MRI data. Her current work focuses on developing new analytic tools for multimodal data to study posttraumatic epilepsy and identify biomarkers of epileptogenesis. The innovative mathematical and statistical techniques that she has developed show significant, promising contributions to visualize complex associations among multiple variables. Additionally, she has also developed and used virtual and augmented reality technology for both data visualization and interaction.

The postdoc will work on multimodal signal analysis and data fusion for post-traumatic epilepsy. The objective of this project is to predict the onset of epileptic seizures following traumatic brain injury using innovative analytic tools from machine learning and applied mathematics to identify features of epileptiform activity from a multimodal dataset collected from both an animal model and human patients. The data are collected from the ongoing Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), which aims to investigate the development of post-traumatic epilepsy following traumatic brain injury in both patients and in a standardized rat model (injury induced with a fluid percussion model). The postdoc will work on discovering salient and robust features of epileptogenesis following traumatic brain injury from this rich dataset as it is being acquired by investigating state-of-the-art models, methods, and algorithms from contemporary machine learning theory. This secondary use of data to support automated discovery of reliable knowledge from aggregated records of animal model and human patient data will lead to innovative models to predict post-traumatic epilepsy. This machine learning based investigation of a rich dataset complements ongoing data acquisition and classical biostatistics-based analyses and can lead to rigorous outcomes that lead to the development of antiepileptogenic therapies, which can prevent this disease.

Preferred Qualifications:

  • Ph.D. in engineering, applied mathematics, or a related area
  • Strong research skills
  • Ability to work independently
  • Strong skills working with big data and analysis
  • Machine learning skills
  • Experience with multimodal data, including neuroimaging, EEG, and behavioral/clinical data
  • Good communication skills
  • High level of motivation
  • Effective problem solving/critical thinking skills
  • Expertise in programming and MATLAB
  • Strong publication record

Minimum Education: PhD or equivalent doctorate within previous three 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 Minimum Experience: - Analysis - Assessment/evaluation - Communication - written and oral skills - Organization - Planning - Problem identification and resolution

REQ20077998 Posted Date: 08/01/2019

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