Research Assistant (Echo Foundation Model)

Job Title: Research Assistant (Echo Foundation Model)

University-Level Unit: College of Design and Engineering

Faculty/Department-Level Unit: Biomedical Engineering

Employee Category: Research Staff

Location_ONB: Kent Ridge Campus

Posting Start Date: 14/05/2026

Job Description

We aim to develop advanced echo foundation model for creating cardiac digital twins, i.e., personalized virtual heart models. We will employ the echo image and ECG collected from patients, to accurately modelling the anatomy and simulate the function of patients' diseased hearts. These models, coupled with machine learning techniques, contribute to the identification of crucial mechanistic relationships and features that offer insights into the trajectory of a patient's heart condition. The research will delve into the intersection of AI and cardiac sciences, exploring novel approaches to revolutionize our understanding of the human heart. With the potential to impact medical treatments and technology advancements, it promises an exciting and important avenue for personalized medicine.

The selected individual will be required to:

Develop and validate novel echo foundation model using Singapore cohort.

Design, implement, and benchmark deep learning models for cardiac mesh reconstruction, segmentation, and functional simulation from echo.

Collaborate with clinicians and data scientists to translate AI-driven tools into actionable clinical insights and workflows.

Publish research in top-tier journals and conferences and contribute to open-source software in echo foundation model.

Qualifications

Possess at least a recognised degree in biomedical engineering, computer science, data science, applied statistics/ mathematics, or any-related field.

Being self-motivated and enthusiastic about AI for healthcare.

Strong problem-solving abilities and learning capability.

Proficiency in programming (Python, C++, etc).

Strong written and spoken communication skills.

First-authored relative publication in top-tier journals and conferences are preferred.

Open to fixed-term contract.