The Data Analytics and Machine Learning (DAML) Group is seeking an exceptional mathematician to work on the development of rigorous theory and algorithms needed for the design and analysis of computational methods that accelerate data analytics and machine learning, especially as the apply to scalable high-performance computing, cloud computing, and large interconnected experimental facilities. You will be responsible for developing energy-efficient, physics-aware algorithms designed for distributed learning across both high-performance and edge computing environments. You will focus on designing architectures and models that effectively capture the complexities of data, provide robust confidence estimates in predictions, and generate compressed quantities of interest tailored to defined domains. Additionally, the role involves creating fast and scalable algorithms capable of fitting the proposed models to data, accompanied by a theoretical framework that elucidates the convergence and effectiveness of these techniques. You will also undertake detailed re-analysis of the trained models' performance, with the goal of uncovering the underlying processes governing the behavior of the given data. This job offers an excellent opportunity to conduct exceptional and innovative research in mathematics, statistics and scientific computing, for applications with scientific and national priority. ORNL's mathematics research efforts provide the fundamental mathematical methods and algorithms needed to model complex physical, chemical, and biological systems. ORNL's computational science research efforts enable scientists to efficiently implement these models at the extreme scale of computing and to store, manage, analyze, and visualize the massive amounts of data that result. ORNL's artificial intelligence research provides the techniques to link the data producers, e.g., supercomputers and large experimental facilities, with the data consumers, i.e., scientists who need the data.