The USC Mark and Mary Stevens Neuroimaging and Informatics Institute and Laboratory of Neuro Imaging (INI, www.ini.usc.edu) are world leaders in developing 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 integrating 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, Neuroscience, and Biomedical Engineering at LONI. Her work combines 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 as part of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx). The innovative mathematical and statistical techniques that she has developed show significant, promising contributions to visualize complex associations among multiple variables. Her lab works on developing large-scale, multimodal data repositories, such as the Data Archive for the BRAIN Initiative (DABI) and the COVID-19 Data Archive (COVID-ARC). She has also developed and used virtual and augmented reality technology for data visualization and interaction.

Responsibilities: The postdoctoral scholar will work on DABI, a web-accessible data archive that captures, stores, and curates human invasive neurophysiology data from NIH Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative proposals.

The ideal candidate will have experience in signal processing, imaging, and machine learning to analyze data and help with aggregation, harmonization, and dissemination of our datasets to the research community. More specifically, they will be able to understand, process, and analyze electrophysiology data to help update and design new neural signal processing tools that can accommodate a wide variety of electrophysiology modalities and formats. Further, they will be able to conduct regular electrophysiology analysis workshops for DABI researchers to help target secondary data analysis issues for publications.

The postdoctoral scholar will design secondary analysis protocols and conduct a robust analysis of DABI data to propose data pooling techniques, harmonization and standardization protocols, analysis tools improvements, and machine learning algorithms to produce a wide range of DABI data-based publications.

The candidate will attend conferences and give presentations about secondary analysis, DABI data, and tools to increase researchers’ interest in storing and re-using DABI data.

In addition, the ideal candidate will have experience working with convolutional neural networks, deep learning toolboxes and have programming skills in MATLAB and/or Python. The role will also include curating datasets and performing data harmonization, quality control, and aggregation. The candidate will maintain communication and provide informatics expertise to collaborating investigators. Other responsibilities will include training and mentoring student researchers and interacting with data providers. The candidate should have a strong focus on proposing and contributing to publications.

Strong knowledge of statistical methods for neurodata analysis is preferred.

Preferred Qualifications:

Minimum education: Doctoral degree in biomedical engineering, neuroimaging, neuroinformatics, or a related field

Experience with neuroimaging data analysis

Experience with programming in MATLAB, R, and Python

The annual base salary range for this position is $62,400.00- $70,000.00. When extending an offer of employment, the University of Southern California considers factors such as (but not limited to) the scope and responsibilities of the position, the candidate’s work experience, education/training, key skills, internal peer equity, federal, state, and local laws, contractual stipulations, grant funding, as well as external market and organizational considerations.

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