The Center for Applied Scientific Computing (CASC) within the Computing Directorate, is seeking a Postdoctoral Research Staff Member with a strong background in computational science, scientific machine learning, reduced-order modeling, or data-driven modeling of physical systems. You will conduct research on the development of fast, trustworthy, and data-efficient surrogate models that integrate physics-based simulations with experimental data and enable artificial intelligence (AI)-agent-assisted scientific workflows. This position will contribute to multidisciplinary research connecting physical experiments, high-fidelity computational models, surrogate and reduced-order models, and AI agents within automated Design-Build-Test-Compute workflows. Research opportunities include developing methods to reconcile discrepancies between computational models and physical experiments, constructing surrogate models from sparse experimental data, identifying low-dimensional representations of high-dimensional parameter spaces, developing uncertainty-aware and adaptive models, and integrating computational models with AI agents for scientific decision support. Applications will include electrochemical systems and advanced manufacturing, with opportunities to develop broadly applicable methods and software for computational science. You will Conduct research and development in one or more of the following areas: scientific machine learning, reduced-order modeling, surrogate modeling, system identification, equation discovery, uncertainty quantification, active learning, optimization, computational mechanics, and data-driven modeling of physical systems. Develop data-efficient computational methods for constructing predictive models from combinations of high-fidelity simulation data and sparse, noisy, or time-dependent experimental observations. Develop approaches for reconciling computational models with physical experiments, including parameter calibration, model-discrepancy correction, equation discovery, and hybrid physics/data-driven modeling. Develop methods for identifying low-dimensional parameter spaces, latent representations, or active subspaces that enable efficient surrogate construction and experimental exploration. Develop uncertainty-aware surrogate models and methods for assessing model validity, detecting out-of-distribution operating conditions, and determining when model predictions can be reliably used for scientific decision support. Investigate active-learning and adaptive experimental-design strategies in which computational models and AI agents identify informative operating conditions or experiments for improving surrogate models. Develop and integrate surrogate and reduced-order models with AI-agent-compatible software interfaces to enable automated model execution, model updating, optimization, and decision support. Apply developed methods to multidisciplinary applications involving physical experiments and computational simulations, including electrochemical systems and advanced manufacturing. Design and perform numerical experiments to evaluate model accuracy, computational performance, data efficiency, uncertainty, robustness, and generalization. Contribute to the development and utilization of LLNL computational tools for reduced-order modeling and scientific machine learning. Contribute to and actively participate in the conception, design, and execution of research addressing defined scientific and engineering problems. Pursue independent but complementary research interests and interact with a broad spectrum of scientists and engineers internally and externally to the Laboratory. Collaborate with experimentalists, computational scientists, applied mathematicians, software developers, and AI researchers in a multidisciplinary team environment. Publish research results in peer-reviewed scientific or technical journals and present results at external conferences, seminars, and technical meetings. Perform other duties as assigned.