Post Doctoral Associate-Bokai Zhu Lab

University of Pittsburgh • Pittsburgh, PA

78d

About The Position

The Zhu Laboratory at the University of Pittsburgh School of Medicine and Aging Institute is seeking a highly motivated postdoctoral associate with strong quantitative and theoretical expertise to study the systems biology of proteostasis regulation and biological timing. Our lab recently discovered a cell-autonomous 12-hour proteostasis oscillator that coordinates stress responses, protein quality control, and metabolism. We now aim to understand how the interplay between molecular chaperone networks, unfolded protein response (UPR) dynamics, and stochastic proteostatic fluctuations determines cell fate decisions in health, aging, and neurodegeneration. This interdisciplinary project combines mathematical modeling, quantitative live-cell imaging, and molecular cell biology to uncover the fundamental principles that govern proteostasis oscillations. The successful candidate will develop both deterministic and stochastic ODE models to describe the temporal organization of proteostasis networks and their coupling to the 12-hour nuclear speckle–UPR feedback loop. These models will be integrated with single-cell time-lapse imaging datasets to quantitatively characterize how distinct proteostasis and stress response trajectories influence survival, adaptation, or senescence.

Requirements

• Ph.D. in Systems Biology, Computational Biology, Applied Mathematics, Biophysics, Bioengineering, or a related quantitative field.
• Proven experience in mathematical or computational modeling (ODEs, stochastic simulations, dynamical systems).
• Familiarity with live-cell imaging, image analysis, and single-cell data quantification is strongly desired.
• Proficiency in Python, MATLAB, or equivalent programming environments for modeling and data analysis.
• Strong collaborative and communication skills, with enthusiasm for bridging theory and experiment.

Nice To Haves

• Experience with machine learning, Bayesian inference, or data-driven modeling approaches is advantageous but not required.

Responsibilities

• Develop and analyze deterministic and stochastic models (ODEs, stochastic simulations, hybrid models) of the 12-hour proteostasis oscillator.
• Collaborate closely with experimental biologists to integrate live-cell imaging and transcriptomic data into quantitative frameworks.
• Apply statistical inference, data assimilation, and/or machine learning methods to extract dynamic parameters from imaging datasets.
• Design and perform imaging-based assays to monitor stress and UPR dynamics at single-cell resolution, using fluorescent reporters and live-cell microscopy.
• Present findings at national conferences and contribute to high-impact publications and grant proposals.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees