Postdoc in AI-Driven Medical Ultrasound for Neonatal Growth

Boston College; Newton (MA); On-site; USD 67,000 - 77,000; Full time

Benefits offered by this job

Tuition remission for employees

Tuition remission for spouses and/or /

Medical, dental, and vision insurance

Retirement plans (401k/403b)

Paid holidays

Sick and vacation pay

Job summary

Boston College seeks a Postdoctoral Research Fellow in the Engineering department to lead analytic and ML components for a NIH NICHD-funded project on AI-enabled portable ultrasound methods to assess body composition and growth in preterm infants. The fellow collaborates with Brigham and Women's Hospital, U Minnesota, and BC faculty, co-mentored by Dr. Ranger and Dr.

Yuan Yuan. The role focuses on integrating ultrasound with clinical and maternal data, developing predictive models, and advancing

Qualifications

• Ph.D. in Computer Science, Biomedical Engineering, or related field.
• Strong research background in machine learning, computer vision, or medical image analysis.
• Experience working with clinical data preferred; familiarity with longitudinal or multimodal data integration a plus.
• Demonstrated research excellence through publications in AI/ML or biomedical imaging venues.

Responsibilities

• Lead data analysis and predictive modeling integrating ultrasound, clinical, and maternal data.
• Apply AI/ML methods including computer vision for real-time scan quality feedback.
• Maintain reproducible, well-documented analysis pipelines and code for public release.
• Supervise and mentor graduate and undergraduate research students.
• Interpret results with clinical aims, ensuring rigor and clinical relevance.
• Contribute to manuscript preparation, conference presentations, and proposal development.

Skills

Machine learning

Computer vision

Medical image analysis

Clinical data

Mentoring

Teamwork

Publications

Multimodal data

Education

Ph.D. in Computer Science or Biomedical Engineering

Job description

Boston College seeks a Postdoctoral Research Fellow in the Engineering department to lead analytic and ML components for a NIH NICHD-funded project on AI-enabled portable ultrasound methods to assess body composition and growth in preterm infants. The fellow collaborates with Brigham and Women's Hospital, U Minnesota, and BC faculty, co-mentored by Dr. Ranger and Dr.

Yuan Yuan. The role focuses on integrating ultrasound with clinical and maternal data, developing predictive models, and advancing