Postdoctoral Fellow - AI-Enabled Immune Digital Twin Workflows, Data Assimilation, and Ensemble Updating

Title
Postdoctoral Fellow - AI-Enabled Immune Digital Twin Workflows, Data Assimilation, and Ensemble Updating

Position Summary
Your primary duties will be to build reproducible, testable, sequential data-assimilating digital twin workflows under the direction of Dr. James A. Glazier. You will implement patient-specific forecasting and rebasing pipelines, design anomaly-detection and residual-monitoring logic, infer unobserved state parameters, manage complex model ensemble interfaces, and coordinate computational infrastructure with national consortium collaborators.

60% Digital Twin Workflows & Data Assimilation: Develop Python workflows for short-horizon forecasting and patient-specific rebasing; implement data-assimilation methods (Kalman/Bayesian updating); design anomaly detection and missing-information inference logic.

40% Software Engineering & Integration: Build reproducible computational packages(CI/CD, containers, testing); manage ensemble interfaces; integrate workflows with external consortium platforms (Vivarium, QSP); write technical publications.

Basic Qualifications
Ph.D. or equivalent terminal degree in quantitative, computational, or biomedical discipline.

Programming proficiency in Python, Julia, R, or MATLAB; background in mechanistic modeling (ODE/dynamical systems, QSP, or agent-based); familiarity with inflammation, cytokine signaling, sepsis, or critical care biology.