Assistant Professor in Medical Image Processing and Machine Learning

The Imaging Computing Laboratory at the Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine is looking for candidates to fill a position in medical image processing and machine learning. This position is in the non-tenure track. The research focuses will include: machine learning-based arterial spin labeling perfusion MRI denoising, MRI based clustering. We are particularly interested in sparsity learning, matrix decomposition, and deep machine learning. Ideal candidates should have a PhD in computer science, biomedical engineering, applied mathematics, or electrical engineering. Over three years of research experience in medical image processing is preferred.

The Imaging Computing Lab is developing advanced medical image processing techniques, imaging acquisition methods, and machine learning methods for neuroscientific and clinical research. We currently have 9 lab members and will have 3 more by the summer. Details about the research in the lab can be found in https://www.medschool.umaryland.edu/pi/Ze-Wang-PhD

Competitive salary and comprehensive benefits are proudly offered. Faculty rank is commensurate with candidate’s qualifications and experience. Please send CV with cover letter of interest to the attention of Barbara Stewart at bstewart@umm.edu , or mail:

Department of Diagnostic Radiology and Nuclear Medicine

University of Maryland Medical Center, Rm N2E23

22 South Greene Street

Baltimore, MD 21201

Qualifications :

The University of Maryland, Baltimore is an Equal Opportunity, Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law or policy. We value diversity and how it enriches our academic and scientific community and strive toward cultivating an inclusive environment that supports all employees.