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Artificial Intelligence applied to Infectious Disease ImagingPosted by: National Institutes of Health (NIH)
Posted date: 2019-Feb-14
The NIAID Integrated Research Facility (IRF), (Frederick, Maryland), the Center for Infectious Disease Imaging in the Clinical Center (CC), and the Frank Lab (CC), NIH are accepting applications for a two-three-year research fellowship position for qualified Ph.D., or MD/Ph.D. candidates with a proven background in designing and deploying artificial intelligence/machine learning algorithms with a focus on medical imaging. The researcher will work collaboratively within our expanding program in molecular and structural imaging of infectious diseases. This research will explore the development and application of machine learning algorithms and predictive models to train and perform accurate organ segmentation of medical images, and to integrate imaging and other core data for the study of several aspects of high consequence viral disease including pathogenesis, diagnosis, vaccines, and therapeutics.
State-of-the-art imaging modalities include PET-CT, SPECT-CT, ultrasound, micro PET-CT, optical imaging, and MRI in a BSL-4 environment. The NIH high performance computing group supports Biowulf, a 90,000+ processor Linux cluster, and provides bioinformatics training and support services. Currently, we are studying imaging, clinical, and laboratory markers of disease progression in a number of experimental preclinical models for filoviruses (Ebola, Marburg), Lassa virus, and Nipah virus, with opportunities to study other BSL-4 agents. The researcher will have the opportunity to work directly with world-class radiologists, virologists, pathologists, immunologists, and data scientists to develop segmentation and quantification methods, and explore early imaging biomarkers of immune and other host system responses.To Apply:
Candidates should send a cover letter, curriculum vitae, and names and contact information for three references to:
Joseph A. Frank MD MS
The NIH is dedicated to building a diverse community in its training and employment programs.