Duke University, Department of Mathematics

Position ID:

Duke-POSTDOCASSOC [#26970]

Position Title:

Postdoctoral Associate – Scientific Machine Learning for Multiscale Biological Systems

Position Type:

Postdoctoral

Position Location:

Durham, North Carolina 27708, United States of America

Appl Deadline:

(posted 2025/09/04, listed until 2026/02/20)

Position Description:

Postdoctoral Associate – Scientific Machine Learning for Multiscale Biological Systems Duke University – Departments of Mathematics and Biostatistics & Bioinformatics

Duke University invites applications for a postdoctoral associate in scientific machine learning (SciML) as part of a new NIH-funded Center for Excellence in Multiscale Immune Systems Modeling. This position focuses on leveraging and developing new equation learning methods, such as Physics-Informed Neural Networks (PINNs), Biologically Informed Neural Networks (BINNs), and sparse-regression based techniques to derive interpretable and computationally efficient differential equation models from computationally intensive multi-cellular agent based models (ABMs) of Epstein–Barr Virus (EBV) and HIV-1 infection dynamics in human lymphoid tissue. This postdoctoral associate will collaborate with experimentalists to utilize EBV and HIV-1 infection data together with multiscale ABM simulations to identify key mechanistic drivers of viral persistence and immune response, and use SciML to automatically select ODE/PDE models that include these mechanisms. The postdoc will develop biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM data and spatial-omics data collected from state-of-the-art microfluidic lymph node on-a-chip systems.

The postdoc will be co-mentored by an interdisciplinary team of biologists and mathematicians including:

• Dr. Kevin Flores (Mathematics, North Carolina State University)

• Dr. Micah Luftig (Molecular Genetics & Microbiology, Duke University)

• Dr. Cliburn Chan (Biostatistics & Bioinformatics, Duke University)

• Dr. Jianfeng Lu (Mathematics, Duke University)

• Dr. Veronica Ciocanel (Mathematics, Duke University)

• Dr. John Hickey (Biomedical Engineering, Duke University)

• Dr. Jessica Conway (Mathematics & Biology, Penn State University)

• Dr. Elliott SoRelle (Microbiology & Immunology, University of Michigan)

Responsibilities:

• Develop SciML methods for learning ODE, PDE, and stochastic models from ABM simulations and multiscale spatial-omics data. • Integrate uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in the NIH Center to iteratively refine learned models.

Qualifications:

• Ph.D. in applied mathematics, computational science, statistics, machine learning, or related quantitative field. • Proficiency with deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in interpretable ML and mechanistic model discovery.

Submit a cover letter, CV, research statement, and three reference letters. Review of applications will begin immediately and continue until the position is filled.

We are not accepting applications for this job through MathJobs.Org right now. Please apply at https://academicjobsonline.org/ajo/jobs/30408 .

Contact: Appointments Committee, 919-660-2800

Email: email address

Postal Mail:

: Appointments Committee
Department of Mathematics
Box 90320 Duke University
Durham, NC 27708

Web Page: http://www.math.duke.edu