University of Maryland, Baltimore
Asst Prof Machine Learning
University of Maryland, Baltimore, Baltimore, Maryland, United States, 21276
Overview
The Imaging Computing Laboratory at the Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine is looking for an Assistant Professor (non-tenure track) in machine learning based medical image processing. Research focuses include: deep learning-based MRI image reconstruction, deep learning based arterial spin labeling perfusion MRI processing, and resting state fMRI processing. Ideal candidates should have a PhD in computer science, biomedical engineering, or electrical engineering. Over three years of research experience in deep learning, MRI and medical image processing is preferred. Responsibilities
Conduct research in machine learning for medical image processing, including MRI reconstruction and fMRI processing. Collaborate with clinical and research teams to advance imaging techniques and translational impact. Publish findings in peer‑reviewed venues and contribute to grant proposals where appropriate. Qualifications
PhD in computer science, biomedical engineering, electrical engineering, or a closely related field. Relevant experience in deep learning, MRI, and medical image processing (preferred: >3 years). How to Apply
Candidates should submit a full CV, a cover letter, and names of three referees who can provide letters of recommendation to ze.wang@som.umaryland.edu. For immediate consideration, please send a cover letter and a recent CV, including names and contact information of three references, to the following link: https://umb.taleo.net/careersection/jobdetail.ftl?job=240000I7&lang=en Equal Opportunity
UMB 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 strive toward cultivating an inclusive environment that supports all employees.
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The Imaging Computing Laboratory at the Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine is looking for an Assistant Professor (non-tenure track) in machine learning based medical image processing. Research focuses include: deep learning-based MRI image reconstruction, deep learning based arterial spin labeling perfusion MRI processing, and resting state fMRI processing. Ideal candidates should have a PhD in computer science, biomedical engineering, or electrical engineering. Over three years of research experience in deep learning, MRI and medical image processing is preferred. Responsibilities
Conduct research in machine learning for medical image processing, including MRI reconstruction and fMRI processing. Collaborate with clinical and research teams to advance imaging techniques and translational impact. Publish findings in peer‑reviewed venues and contribute to grant proposals where appropriate. Qualifications
PhD in computer science, biomedical engineering, electrical engineering, or a closely related field. Relevant experience in deep learning, MRI, and medical image processing (preferred: >3 years). How to Apply
Candidates should submit a full CV, a cover letter, and names of three referees who can provide letters of recommendation to ze.wang@som.umaryland.edu. For immediate consideration, please send a cover letter and a recent CV, including names and contact information of three references, to the following link: https://umb.taleo.net/careersection/jobdetail.ftl?job=240000I7&lang=en Equal Opportunity
UMB 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 strive toward cultivating an inclusive environment that supports all employees.
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