Ideal candidates will have a robust mathematical background and have expertise in one or more of the following areas (not exhaustive): Uncertainty quantification, stochastic analysis, or probabilistic modeling Statistical learning theory, high dimensional inference, or generalization/robustness PDEs, dynamical systems, or stochastic differential equations especially as they interact with learning Graph and network methods, algorithmic combinatorics, discrete optimization, or network science Principled methods for interpretability, certification, fairness, or robustness in AI. Teaching: Deliver undergraduate core mathematics courses (1000-3000 levels), courses within the interdisciplinary/applied mathematics graduate core (see USU General Catalog at https://catalog.usu.edu/ and search courses with prefix MATH, numbered 5000+), and develop advanced courses in AI, machine learning, computational mathematics, and data science.