The Center for Hierarchical and Robust Modeling of Non-Equilibrium Transport (CHaRMNET), housed in the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University, seeks to hire a postdoctoral research associate in the broad area of computational physics (https://charmnet-mmicc.github.io). This is a one-year, benefits-eligible position that is potentially renewable on an annual basis contingent upon satisfactory performance and availability of funding. The mission of CHaRMNET is to develop novel numerical methods that allow us to overcome the curse of dimensionality in the simulation of fusion energy systems. The curse of dimensionality refers to the fact that traditional methods scale as O(Nd), where N is the number of degrees of freedom per dimension d. Foundational kinetic models often have N∼256 and d≥6, making direct numerical simulation intractable with traditional approaches. Through innovative work combining machine learning with new paradigms for direct solvers of high-dimensional partial differential equations, members of CHaRMNET aim to overcome this challenge. The focus of this position is on structure-preserving, machine-learning-accelerated scientific computing for plasma physics applications. In particular, the project involves developing data-driven collisional kinetic models and numerical schemes that preserve physical structures such as conservation laws and entropy dissipation. The target application is enabling scale-bridging kinetic models for fusion systems. This work encompasses both research in numerical methods for partial differential equations and machine learning methods for physics-based modeling.