Postdoctoral Research Associate, Radiology and Imaging Services

The Department of Radiology & Imaging Sciences at the University of Arizona invites applications for a Postdoctoral Scholar with expertise in MR pulse sequence development and advanced image reconstruction. The successful candidate will contribute to NIH-funded research focused on developing and validating quantitative magnetic resonance imaging (qMRI) methods and applying these techniques to clinically relevant problems.

Interested candidates are encouraged to apply and will join an active team focused on cutting-edge quantitative MRI method development and clinical translation.

• Visa sponsorship is not available for this position.

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• Design,
  implement, and optimize MR pulse sequences on Siemens platforms for
  quantitative imaging.
• Develop,
  adapt, and evaluate modern reconstruction algorithms for accelerated and
  robust qMRI.
• Collaborate
  with a multidisciplinary team to translate methods to clinical research
  studies and analyze imaging data.
• Disseminate findings through peer-reviewed
  manuscripts, conference presentations, open/reproducible code, and the
  development and distribution of Siemens Work-In-Progress (WIP) packages where
  appropriate.

• Ph.D.
  in Electrical Engineering, Biomedical Engineering, Applied Mathematics, or
  a closely related field.
• Demonstrated
  experience with Siemens MR pulse sequence programming (e.g., IDEA/ICE) and
  contemporary image reconstruction techniques (e.g., compressed sensing,
  parallel imaging, model-based or deep learning reconstructions).
• Knowledge of radial data acquisition strategies,
  artifact mitigation methods, and their use in parametric imaging (e.g.,
  T1/T2/T2* mapping).

• Strong programming skills (e.g., C/C++, Python, MATLAB) and familiarity with scientific software development best practices.
• Evidence of scholarly productivity and effective collaboration in interdisciplinary teams.

Maria Altbach, maltbach@arizona.edu

Maria Telles, telles@arizona.edu

Application: The online application should be completed in its entirety. Blank or missing information may be considered an incomplete submission.

Cover Letter: Should clearly indicate how your skills and professional employment experience meet the Minimum and the Preferred qualifications (if applicable).