JOB TITLE Post-Doctoral Fellow LOCATION Worcester DEPARTMENT NAME Biomedical Engineering - NFR JM DIVISION NAME Worcester Polytechnic Institute - WPI JOB DESCRIPTION SUMMARY The Artificial Intelligence and Modeling Laboratory for Cardiovascular Diseases (AIMCardio Lab) at Worcester Polytechnic Institute (WPI) is seeking applications for two postdoc research positions. We are looking for highly motivated,
collaborative, and enthusiastic researchers with expertise in fluid-structure interaction, computational and experimental fluid mechanics, and/or scientific machine learning, with applications to cardiovascular diseases and medical devices. The initial appointment is for one year, with the possibility of renewal based on performance and funding availability. Successful candidates are expected to commit to a minimum
two-year appointment. JOB DESCRIPTION AIMCardio Lab The lab conducts cutting-edge research in computational and experimental fluid-structure interaction, cardiovascular biomechanics, digital health, and AI-driven modeling. The laboratory maintains long-standing collaborations with leading hospitals and medical centers across the United States, including the Hospital for Sick Children (Toronto), Children’s Hospital of
Philadelphia (Philadelphia), and Columbia University Irving Medical Center (New York). We also collaborate closely with major medical device companies, including Abbott, Edwards Lifesciences, Boston Scientific, and Medtronic, to develop next-generation cardiovascular technologies and translational solutions. More information about our research can be found at: AIMCardioLab.wpi.ed u Required Qualifications PhD. in
Mechanical Engineering, Biomedical Engineering, Aerospace Engineering, Electrical Engineering, Applied Mathematics, Computer Science, or a closely related field. Demonstrated research excellence through publications for postdoc applicants in one of the following areas: Experimental fluid mechanics and advanced flow visualization techniques (e.g., Particle Image Velocimetry (PIV), flow imaging, or related experimental
methods). Fluid-structure interaction (FSI), computational fluid dynamics (CFD), finite element analysis (FEA), or multiphysics simulations, with strong programming and computational skills. Scientific machine learning , including physics-informed neural networks, operator-learning methods, surrogate modeling, and data-driven modeling of physical systems. Preferred Qualifications Experience conducting research in
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