Postdoctoral Scholar-Fixed Term
About The Position
The successful candidate will work with Dr. Kunlun Qi on research projects related to kinetic equation and its related multiscale model reduction, numerical method, and data-driven/machine-learning approaches. The research areas include numerical simulation of kinetic equations with fast algorithms and stability/convergence analysis; data-driven and machine learning-assisted methods such as data assimilation, uncertainty quantification (UQ), and machine-learning-based moment closure models; multiscale modeling including kinetic limits of many-particle dynamical systems, hydrodynamic limits of kinetic models, and semiclassical limits of quantum systems; and theoretical analysis for kinetic PDEs, focusing on well-posedness and asymptotic behavior of the Boltzmann equation and related models.
Requirements
- Ph.D. in Mathematics, Statistics, or a closely related field by the start of the appointment.
Nice To Haves
- Strong backgrounds in kinetic equation, numerical analysis, scientific computing, PDE
- Research experience in scientific machine learning
Responsibilities
- Numerical simulation of kinetic equations: fast algorithms with stability/convergence analysis
- Data-driven and machine learning-assisted methods: data assimilation, uncertainty quantification (UQ), and machine-learning-based moment closure models
- Multiscale modeling: kinetic limits of many-particle dynamical systems, hydrodynamic limits of kinetic models, and semiclassical limits of quantum systems
- Theoretical analysis for kinetic PDEs: Well-posedness and asymptotic behavior of the Boltzmann equation and related models
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
