Postdoctoral fellowship in deep learning methods in quantitative MRI in pediatric applications (Harvard)
Wednesday 5th August 2020
Postdoctoral fellowship in deep learning methods in quantitative MRI in pediatric applications
Position description: The Computational Radiology Laboratory (CRL) directed by Prof. Simon Warfield at Boston Children’s Hospital and Harvard Medical School has an immediate opening for a highly motivated post-doctoral research fellow to develop motion-compensated image reconstruction algorithms, automated image analysis using deep learning tools and motion-robust quantitative MR imaging techniques to compute imaging markers of disease from MR images of abdomen and brain for projects funded by the National Institutes of Health and other funding mechanisms.
The researcher will develop highly innovative imaging and image processing techniques and software tools including image segmentation, registration approaches, machine learning algorithms to analyze images, correct for motion and distortion, and reconstruct high-resolution images and estimate quantitative model parameters to generate marker of disease in using several quantitative MRI techniques including DCE-MRI and diffusion-weighted MRI. The researchers will have access to radiologists, MR physicists, will have mentorship from an expert team of principal investigators, and will directly work under the supervision of Dr. Sila Kurugol and Dr. Onur Afacan (faculty members at Harvard Medical School), and will attend courses and seminars offered at Boston Children’s Hospital, Harvard Medical School, and Harvard Catalyst.
About CRL: The mission of CRL is improving our understanding of the structure and function of the brain and other organs of the human body, in order to improve our capacity to diagnose and treat disease. CRL is located at Boston Children’s Hospital (BCH), which is ranked #1 in US News ranking of pediatric hospitals. Researchers at CRL are affiliated with Harvard Medical School which is ranked #1 in US News ranking of medical schools. Boston is known as a hub for healthcare and medical innovations, and a beautiful city full of top-rank universities. Opportunities for cross-training and networking are enormous with several top-rank universities (Harvard, MIT, Northeastern, and Boston University), top-rank hospitals and medical research centers, and biotechnology and pharmaceutical companies in the neighborhood.
• PhD in electrical or biomedical engineering, computer science, medical physics, or a related field with a research focus on biomedical image processing/medical image computing, machine learning, computer vision, and/or magnetic resonance imaging
• Demonstrated record of high-performance scientific programming with python and/or C++
• Demonstrated record of high-quality publications in the field
• Demonstrated analytical, verbal, and scientific writing skills
Highly desired qualifications:
• Experience in machine learning/ deep learning in MRI
• Experience in MR image acquisition
• Experience in MR image reconstruction
• Experience in MR image analysis
To apply: Please send your CV along with a copy of a technical paper, the desired start date, a "paragraph" including statement of goals and three references to Dr. Sila Kurugol (email@example.com) and Dr. Onur Afacan (firstname.lastname@example.org). Computational Radiology Laboratory, Boston, USA.
|Title||Postdoctoral fellowship in deep learning methods in quantitative MRI in pediatric applications|