The Department of Aerospace Engineering at San Diego State University invites 
applications for a postdoctoral researcher position to work on cutting-edge 
methods for data assimilation in chaotic nonlinear systems, with a focus on 
machine learning and variational methods. The goal is to 
develop novel machine learning-based algorithms that are tailored for the 
characteristics of turbulent flows. This is a collaborative project with the 
Johns Hopkins University.
Qualifications:

Ph.D. (or nearing completion) in applied mathematics, engineering, physics, or a 
related field.
Strong background in at least one of the following: turbulence, adjoint-based 
optimization, inverse problems, machine learning, or nonlinear dynamics.
Proficiency in scientific computing and programming (e.g., Python, 
TensorFlow/PyTorch, JAX).
Demonstrated ability to conduct independent research and collaborate in a 
multidisciplinary team.
Applicants must be currently eligible to work in the United States. We are 
unable to sponsor visas at this time.
Start Date: Preferably around July 2025.

Application Materials:
Please submit the following via email to qwang4@sdsu.edu:

CV
Brief statement of research interests and experience
Contact information for 2–3 references
Review of applications will begin immediately and continue until the position is 
filled.