Postdoctoral Research Fellow in AI for Medical Ultrasound Image Analysis

Job Title: Postdoctoral Research Fellow Department: Engineering Employment Type: Onsite | Full-Time (35 Hours per week) | Temporary FTE Range: $67,000 - $76,962 based on experience Postdoctoral Research Fellow in AI for Medical Ultrasound Image Analysis The Biomedical Imaging and Instrumentation Lab in the Department of Engineering at Boston College (bc.

edu/ranger-lab), led by Prof. Bryan Ranger, is recruiting a Postdoctoral Research Fellow to join a multi-site research project funded by an NIH R01 award from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), developing AI-enabled portable ultrasound methods to assess body composition and growth in preterm infants.

This is an exciting opportunity for a postdoctoral researcher who wants to do meaningful, clinically grounded work in medical imaging AI. This position focuses on leading the analytic and machine learning components of a longitudinal, multi-site study following preterm infants from birth through 24 months. The fellow will work at the intersection of AI/ML, medical imaging, and clinical neonatal research.

They will develop predictive models that integrate ultrasound imaging with clinical and maternal data, in close collaboration with clinical investigators at Brigham and Women's Hospital, the University of Minnesota Masonic Children's Hospital, and the Boston College Connell School of Nursing (CSON), as well as biostatisticians at Boston College.

The fellow will be co-mentored by Dr. Ranger and Dr. Yuan Yuan (Computer Science, Boston College). Why Join This Project Direct clinical collaboration: you’ll work closely with colleagues at the Boston College CSON and with pediatricians at Brigham and Women's Hospital and the University of Minnesota Masonic Children's Hospital.

Opportunities to develop novel machine learning methods: you’ll work with real-world clinical imaging data to devise and validate novel predictive models, from foundation model adaptation to longitudinal trajectory analysis. Leadership opportunities: you’ll take primary responsibility for analytic strategy and model development, with genuine input into the direction of the project's aims as it evolves.

You will also work closely and have opportunities to mentor graduate and undergraduate student researchers. Career development: support for conference travel, opportunity to lead research and be first-author on publications across both the AI and clinical literatures, and direct experience with proposal writing and grant management.

A collaborative, multi-disciplinary, mentorship-focused lab: you'll be co-mentored by faculty in engineering and computer science, with a team that values your growth as a researcher and mentor.

Key Responsibilities

Lead data analysis and predictive modeling efforts that integrate ultrasound, clinical, and maternal data to develop and validate models for body composition and growth outcomes across two clinical sites. Apply advanced AI/ML methods, including computer vision for real-time scan quality feedback, longitudinal trajectory modeling, and uncertainty quantification, in collaboration with the project's biostatistician.

Maintain reproducible, well-documented analysis pipelines and code for public release. Supervise and mentor graduate and undergraduate research students. Interpret results in the context of the study's clinical aims, and work closely with clinical investigators to ensure rigor and clinical relevance. Contribute to manuscript preparation, conference presentations, and proposal development.

This is a full-time, one-year appointment, renewable for additional years based on performance and funding availability. We are looking to fill this position as soon as possible but will remain flexible on specific start date for the right candidate. Applicants should submit: A cover letter describing research interests and fit for the position.

A curriculum vitae (CV). Names and contact information of two references (contacted only for shortlisted candidates). If you are shortlisted for the position, you will be contacted for a virtual interview. For inquiries, please contact Prof. Bryan Ranger ([email protected]).