Research Fellow/Staff Scientist

Beth Israel Deaconess Medical Center, Harvard Medical School
Location: 
Boston, MA
Job Type: 
Full Time
Closing Date: 
Friday, August 31, 2018

Applications are invited for a 2 to 3-year postdoctoral research position in the Cardiovascular Magnetic Resonance Center (https://cardiacmr.hms.harvard.edu/), at Harvard Medical School, Boston, MA, USA. The position is in the area of using machine learning to solve challenging problems in cardiovascular magnetic resonance (CMR) imaging. The candidate will be required to design and develop state-of-the-art deep learning algorithms (e.g. deep auto-encoders, fully convolutional and recurrent neural network models) for a number of CMR applications including, but not limited to image reconstruction, image segmentation, and classification.

The candidate will have the opportunity to build effective neural network models using our large and continually growing CMR database. In addition to the high-performance computing center of Harvard Medical School, the candidate will have the opportunity to use the computational infrastructure in our lab which includes the latest state-of-the-art GPU technologies. The candidate will also benefit from regular interaction with expert researchers in computer science/engineering and cardiology.

Applicants are expected to have the following:
• A Ph.D. degree in Computer Science, Electrical Engineering, Biomedical Engineering, or a related field.
• Strong programming and analytical skills.
• Strong experience and theoretical background in machine learning and statistical analysis.
• Experience in Python, Matlab, and C
• Experience in frameworks such as Theano, Tensorflow, Caffe, and/or Torch

Interested candidates should submit
• A recent CV
• List of Publications
• Contacts of three references including current supervisor

Please apply by email to Dr. Reza Nezafat [rnezafat@bidmc.harvard.edu].
Beth Israel Deaconess Medical Center is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.