Postdoctoral Research Associate

Northeastern University•Boston, MA

About The Position

AI-Driven Ultrasound and Intelligent Sensing: We are seeking a postdoctoral researcher to work on quantitative ultrasound methods, focusing on the application of model-based machine learning techniques to raw ultrasound data. The project involves developing novel algorithms that integrate physics, engineering, and AI to extract meaningful and clinically relevant information directly from raw signals. The work spans algorithm development, advances in model-based AI, and translation to real-world clinical applications, offering an opportunity to contribute to next-generation imaging and diagnostic technologies.

Requirements

Ph.D. in Applied Mathematics, Electrical & Computer Engineering, Computer Science, Industrial Engineering, or a closely related field (obtained by start date).
Demonstrated research experience with quantitative ultrasound methods.
Strong background in model-based or physics-informed machine learning, with experience developing algorithms that integrate physics models, engineering principles, and AI techniques.
Ability to design, implement, and validate novel algorithms for applied or translational research, ideally with relevance to real-world or clinical imaging applications.
Proven research and academic writing experience, including the preparation of high-quality scholarly articles for peer-reviewed journals and conferences.
Experienced in mentoring and supervising students on research projects, with demonstrated leadership skills in guiding collaborative work.
Excellent communication abilities, including fluent spoken English and strong presentation skills, enabling effective dissemination of research outcomes and active engagement in international academic discussions.

Responsibilities

Developing novel algorithms that integrate physics, engineering, and AI to extract meaningful and clinically relevant information directly from raw signals.
Algorithm development, advances in model-based AI, and translation to real-world clinical applications.
Mentoring and supervising students on research projects.
Guiding collaborative work.