Staff Research Associate I/II

UCSFSan Francisco, CA
$29 - $50Onsite

About The Position

The Radiology Department is hiring for a Staff Research Associate (SRA). The SRA will work with state-of-the-art 3T and 7T research MRI scanners, as part of prospective cross-sectional and longitudinal clinical research studies with correlation to genomics and neurocognitive testing. The SRA will have the opportunity to work closely with physicians, surgeons, imaging scientists and engineers, as well as cognitive neuroscientists. The focus of the work will be on acquiring, analyzing, and integrating advanced MR imaging data on patients with neuropsychiatric diseases and/or brain injury as well as control volunteers, including fMRI and diffusion tractography. The emphasis will be on artificial intelligence and machine learning (AI/ML) analysis of large imaging datasets using the latest cloud-based and hybrid neuroinformatics platforms. The knowledge and skills learned provide excellent preparation for medical school or for graduate school in neuroscience or psychology or for industry positions in AI/ML applied to Big Data.

Requirements

  • Experience with state-of-the-art 3T and 7T research MRI scanners.
  • Experience with prospective cross-sectional and longitudinal clinical research studies.
  • Experience with genomics and neurocognitive testing.
  • Experience working closely with physicians, surgeons, imaging scientists and engineers, as well as cognitive neuroscientists.
  • Experience with fMRI and diffusion tractography.
  • Experience with artificial intelligence and machine learning (AI/ML) analysis of large imaging datasets.
  • Experience with cloud-based and hybrid neuroinformatics platforms.

Responsibilities

  • Acquiring, analyzing, and integrating advanced MR imaging data on patients with neuropsychiatric diseases and/or brain injury as well as control volunteers, including fMRI and diffusion tractography.
  • Emphasis on artificial intelligence and machine learning (AI/ML) analysis of large imaging datasets using the latest cloud-based and hybrid neuroinformatics platforms.
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