This project involves stochastic and dynamical modeling, machine learning and control theory. More specific topics include parameter estimation, data assimilation and prediction for complex dynamical systems. The postdoc is expected to develop new mathematical theories and numerical algorithms based on the conditional Gaussian framework to solve high-dimensional complex problems, working with Professor Andrew J. Majda (New York University) and Professor Nan Chen (University of Wisconsin-Madison).

The position will be located in New York. Please apply here: https://apply.interfolio.com/64899

Some of the references are as follows: Majda, Andrew, and Nan Chen. "Model error, information barriers, state estimation and prediction in complex multiscale systems." Entropy 20.9 (2018): 644. Chen, Nan, and Andrew Majda. "Conditional Gaussian systems for multiscale nonlinear stochastic systems: Prediction, state estimation and uncertainty quantification." Entropy 20.7 (2018): 509.

Majda, Andrew J. "Prototype Examples of Complex Turbulent Dynamical Systems." Introduction to Turbulent Dynamical Systems in Complex Systems. Springer, Cham, 2016. 5-11.

Majda, Andrew J., and Di Qi. "Effective control of complex turbulent dynamical systems through statistical functionals." Proceedings of the National Academy of Sciences 114.22 (2017): 5571-5576.

See Professor Andrew J. Majda's webpage for more references (https://math.nyu.edu/faculty/majda/publicationrevised.html)

The deadline for application submission is July 31, 2019.

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