Machine / Deep Learning Post-Doctoral Scholar / Research Scientist

Company: Center for Ultrasound Research and Translation, Massachusetts General Hospital


Position: Machine / Deep Learning Post-Doctoral Scholar / Research Scientist

Contact Information:

Location: Boston, MA

Job Description:

We seek a highly talented and motivated data scientist/engineer to join our collegial and diverse multidisciplinary team. The right candidate will have background in medical image processing and machine learning /deep learning / AI. Our goal is to transform medical ultrasound diagnosis through work that spans hardware and algorithm development focused on image reconstruction and analysis, all with an integrated clinical translational component. This is a unique opportunity to be part of a highly energetic multidisciplinary group working to translate research into clinical care.

The successful candidate will be expected to proactively develop his/her research interests and to work effectively with others toward shared goals. Candidates who seek creative academic careers will have excellent growth opportunities, access to committed mentors, assistance with defining an early career trajectory, and mentored opportunities to apply for career development and research grants. There will be opportunities for co-supervision and mentorship from MIT and Harvard scientists working in close collaboration with CURT physicians and scientists.

The successful applicant will:

  • Work collaboratively with a highly motivated and collegial team of physicians, clinical researchers, computer scientists, and engineers to solve important medical problems in a world-class research facility.
  • Have access to a vast amount of high-quality medical imaging and pathologic data, advanced computing facilities, a friendly environment, and an unparalleled community of basic and clinical scientists.
  • Gain a deep understanding of key problems in medical imaging.
  • Gain knowledge and understanding of important disease processes, including cancer, fibrosis, and inflammation.


  • The ideal candidate will have a PhD in Applied Mathematics, Computational Statistics, Computer Science, Bioengineering, Biomedical Engineering, Electrical Engineering or a related discipline.
  • Strong background in machine learning theory and application.
  • Excellent understanding of statistical processes and its applications in machine learning.
  • Strong communication skills in written and verbal English are required.
  • Proficiency with DL open source tool kits like PyTorch, Tensorflow and/or Keras is required.
  • Proficiency with medical imaging libraries like PyDicom, ITK, Open CV, HDF5 format is required.
  • Proficiency in one or more programming language, especially Python, C++, MATLAB, or R, is required.
  • Experience with Linux and image analysis skills is required.
  • Experience with image processing and clinical translation of imaging technologies is desirable.
  • A demonstrated record of high-quality publications in leading ML/DL conferences like CVPR, NeurIPS, ICLR, MICCAI is a plus.
  • Understanding of self-supervised networks, Generative adversarial networks, and annotation-efficient deep learning algorithms is a plus.
  • Up to date with latest advancements in ML/DL research is a plus.
  • Understanding of ultrasound wave propagation, scattering physics, signal processing and experience with medical ultrasound instrumentation is a plus.

Applicants should send a cover letter describing research experience, interests and goals, a CV with a full list of publications, a link to a GitHub profile (if available), and three references (including one from a current supervisor) to Anthony E. Samir, MD, MPH. Interviews will be conducted by a multi-disciplinary team of clinicians and computer scientists. Salary will be commensurate with experience.

We will assist highly qualified candidates to secure a US work visa. The Massachusetts General Hospital is an Equal Opportunity/Affirmative Action Employer.

Contact: Anthony E. Samir, MD, MPH, (

Expiration date: 07/31/2020