Post-Doctoral Fellow – Autonomous Low-Field MRI

Description

Position Overview:
The Laboratory for Accessible MRI, led by Prof. Sairam Geethanath at Johns Hopkins University, invites applications for a two-year Post-Doctoral Fellowship. The fellow will work on cutting-edge research focused on advancing low-field deep learning methods and implementing autonomous low-field MRI systems to ensure consistent scanner operation and maintain high-quality image acquisition. This project will demonstrate the steps required on the 0.05T scanner (available in the laboratory) to track structural brain changes at par with 3T performance. These innovations are expected to improve the accessibility and affordability of MRI technology globally.

Key Responsibilities:

• Tracking brain changes in youth at 0.05T:
  • Advance existing, in-house super-resolution reconstruction methods at 0.05T.
  • Advance existing in-house denoising methods at 0.05T.
  • Refine segmentation of brain tissue matters at 0.05T using FreeSurfer.
• Scan youths (10 – 17 years) at 0.05T and 3T to demonstrate advances
• Develop and implement autonomous operational strategies for low-field MRI scanners
  • Introduce interventions during scanning to guarantee SNR and magnetic field homogeneity limits
  • Mitigate certain artifacts such as motion, electromagnetic interferences, Gibbs ringing, and wrap-around.
• Test and validate developed methods on the available MRI scanners, including:
• • Two custom-built low-field MRI systems (0.26T and 0.35T).
  • A commercial prototype 50mT Halbach scanner.
• Collaborate with a multidisciplinary team of researchers and engineers.

Qualifications

Key Qualifications:

• Ph.D. in Medical Physics, Biomedical Engineering, Electrical Engineering, Computer Science, or a related field.
• Strong background in MR physics, including pulse sequence design, image reconstruction, and signal processing.
• Proficiency in deep learning techniques, with experience in applying machine learning to medical imaging.
• Experience working with low-field MRI or MRI system development is highly desirable.
• Strong programming skills in Python, MATLAB, or similar.
• Ability to work independently as well as in a collaborative research environment.

Application Instructions

Application Process:
Interested candidates should submit the following documents:

• A detailed CV.
• A cover letter describing your relevant research experience and career goals.
• Contact information for three professional references.

Equal Employment Opportunity Statement

Salary Range

The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University's good faith belief at the time of posting.  Not all candidates will be eligible for the upper end of the salary range.  The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University.

Total Rewards

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Equal Opportunity Employer

The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. To that end, the university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status or other legally protected characteristic. The university is committed to providing qualified individuals access to all academic and employment programs, benefits and activities on the basis of demonstrated ability, performance and merit without regard to personal factors that are irrelevant to the program involved.

Pre-Employment Information

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