PhD Positions in Multiscale Modeling and Scientific Machine Learning for Computational Biomedicine

Project Overview

The BioComp Group in the Department of Mathematics at Rowan University focuses on interdisciplinary research at the interface of applied mathematics, scientific machine learning, multiscale biological modeling, and computational biomedicine. This project involves multiscale modeling of blood flow and red blood cell mechanics, scientific machine learning techniques such as physics-informed neural networks and operator learning, and predictive modeling for blood-related diseases.

What You Will Do

You will develop computational models to simulate cell dynamics and blood flow, integrate microfluidic experimental data into predictive computational frameworks, and apply high-performance GPU computing and numerical simulation methods tailored for biomedical applications. The work will involve programming using Python, MATLAB, C/C++, and machine learning frameworks such as PyTorch and TensorFlow.

Expected Outcomes

The project aims to produce robust predictive models for disease-relevant blood cell behavior, innovative algorithms for scientific machine learning tailored to biological systems, and computational tools validated against experimental microfluidic data. The outcomes could impact understanding and treatment strategies for blood disorders.

Why This Matters

Modeling blood flow and cell mechanics at multiple scales with integrated experimental data can transform computational biomedicine, improving disease diagnostics and treatment. The use of scientific machine learning accelerates model development and prediction accuracy, ultimately benefiting biomedical research and healthcare.