Four Postdoc positions at the intersection of computational physics, applied mathematics, and AI

We have 4 positions open, but 5 distinct but deeply connected topics, and we welcome candidates whose interests cut across more than one of them. Position 1. Formal Verification for Scientific Computing We're looking for a candidate with strong expertise in at least one of: - numerical linear algebra and rigorous numerical analysis; - formalization in Lean or Coq (or a comparable proof assistant); - AI-assisted formalization. Position 2. Data-Driven Turbulence Modeling for Hypersonic Flows We're looking for a candidate with strong expertise in: • turbulence modeling in the RANS setting, ideally for compressible or high-speed flows; • data-driven or machine-learning-augmented closure modeling. A solid foundation in computational fluid dynamics and numerics of compressible flows is essential. We will be collaborating with Prof. Paul Durbin (Iowa State) 3. Feature-based modeling for Turbulent Systems We're looking for a candidate with strong expertise in at least one of: • exact coherent structures / invariant solutions and dynamical-systems approaches to turbulence; • reduced-order modeling and modal decompositions, ideally combined with data assimilation or control. Strong PDE and scientific-computing skills are essential; experience with geophysical flows, plasma/MHD, is a plus. The work is part of a collaboration with Los Alamos National Laboratory, anchored by two demanding demonstration applications: ocean dynamics and magnetic-confinement fusion. 4. Generative AI for Spatio-Temporal Dynamics Diffusion models and flow matching offer probabilistic, physically structured surrogates that generate entire fields rather than point estimates: fast, uncertainty-aware, and naturally suited to the multimodal, multiscale character of physical systems. Your work could include building diffusion- and flow-matching-based models for spatio temporal physical systems: turbulence, multiphysics, materials, and geophysical-scale fields; conditional and guided generation; designing generative models that respect physical constraints; and coupling generative surrogates tightly with classical simulation and statistical inference. We're looking for a candidate with strong expertise in: - diffusion models and/or flow matching, or operator learning with demonstrated application to spatio-temporal dynamics (or a strong generative-modeling background and a clear desire to apply it to physical systems). Solid foundations in probability, and large-scale model training are highly valued. 5. Agentic AI for Science