Job Description

We are seeking a highly motivated Research Fellow to develop cutting-edge Physics-Informed AI models for constructing cardiac digital twins—personalized, mechanistic simulations of the human heart that integrate imaging, ECG, and clinical data. The successful candidate will focus on combining deep learning with biophysical cardiac modeling (e.g., electrophysiology, mechanics) using techniques such as physics-informed neural networks (PINNs) or neural operators to enable accurate, data-efficient, and clinically interpretable simulations. This research aims to advance personalized diagnosis, risk stratification, and treatment planning for cardiac disorders. The project involves close collaboration with international partners from NUS, University of Oxford, Imperial College London, and Fudan University, offering a highly interdisciplinary and innovative research environment at the interface of AI, biomedical engineering, and computational cardiology.
 

Qualifications

• Possess a PhD degree in applied mathematics, computer science, computational physics, biomedical engineering, or a related field. 
• Strong self-motivation and enthusiasm for AI applications in healthcare, particularly in digital twins. 
• Extensive experience in AI, especially physics-informed AI, such as surrogate model. 
• Strong problem-solving skills and a proven research track record, demonstrated by first-author publications in top-tier journals and conferences. 
• Proficiency in programming (Python, C++, MATLAB) and familiarity with computational frameworks (e.g., FEM-based solvers, PINNs, etc.). 
• Excellent written and verbal communication skills, as he/she is required to perform scientific writing and presentations.
• Open to fixed-term contract.
   

More Information

Location: Kent Ridge Campus

Organization: College of Design and Engineering

Department : Biomedical Engineering

Employee Referral Eligible: No

Job requisition ID : 28888