Project Overview

The Wearable Imaging for Transforming Elderly Care (WITEC) interdisciplinary research group (IRG) is committed to creating next-generation wearable imaging technologies tailored for aging populations. By combining expertise in wearable transducers, biomedical imaging, materials science, data analytics, and healthcare innovation, WITEC aims to design solutions that will improve quality of life, support preventative medicine, and reduce healthcare burdens. Specific objectives include the development of wearable ultrasound imaging systems for doing cardiovascular echography, AI-powered diagnostic algorithms, and integration with digital health platforms for real-time monitoring and clinical decision support.

WITEC brings together interdisciplinary researchers from MIT and Singaporean institutions, including hospitals and research agencies. Through deep collaboration with local partners, including academia, clinicians, industry, and policymakers, WITEC will ensure that its technological advancements are innovative, clinically validated, and suitable for industrial scale up.

Key Responsibilities

The WITEC IRG at SMART is seeking a qualified candidate for a Postdoctoral Associate position supervised by Prof. Xuanhe Zhao. This position will entail the following technical responsibilities:

1, Develop advanced AI solutions for analyzing time-series 2D/3D ultrasound data. Perform image segmentation, anomaly detection, and extraction of biomarkers for disease classification. Design deep learning models for multimodal image registration and alignment of ultrasound with ground-truth MRI data.

2,  Apply foundation models to analyze medical imaging data. Develop probabilistic deep learning models to capture temporal patterns in chronic disease progression and treatment response. Ensure model fairness and robustness for reliable clinical use.

3, Develop AI-assisted robotic guidance tools for precise and automated ultrasound acquisition. Interface AI algorithms with robotic systems to adaptively control probe positioning and ultrasound imaging parameters.

4, Design and test intuitive human-machine interfaces to support non-specialist users in operating wearable ultrasound devices. Conduct quantitative and qualitative evaluations of AI models in simulated and real-world clinical scenarios. Integrate imaging, diagnostic reporting, and clinical data into a cohesive platform for intelligent diagnosis decision support. Enable clinicians to make informed decisions in cardiovascular care, improving patient outcomes, care quality, and cost-efficiency.

5. Perform other duties as assigned.

Requirements

1. Ph.D. in Computer Science, Electrical Engineering, Biomedical Engineering, or a related field, with expertise in computer vision or medical image analysis.

2. Demonstrated research experience in 2D/3D medical imaging using deep learning methods. Proved by strong track record of publications in top-tier AI and medical imaging conferences/journals.

3. Solid mathematical background, excellent programming skills, and proficiency in machine learning frameworks such as Python, C/C++, TensorFlow, and PyTorch.

4. Extensive experience in applying advanced machine learning and deep learning techniques to medical image analysis tasks—including, but not limited to, image segmentation, classification, registration, and anomaly detection.

5. Experience in software system engineering and architecture design for AI-based medical applications. Ability to develop immersive, user-friendly interfaces tailored for non-professional healthcare users.

6. The candidate needs to work well in a team with good communication skill.

Interested applicants are invited to send in their full CV/resume, cover letter and list of three references (to include reference names and contact information). We regret that only shortlisted candidates will be notified.