Electronic Structure and Data-Driven Materials Design

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In this position, you will conduct first-principles simulations and data-driven analyses to understand and design catalytic materials with targeted redox and adsorption behavior. You will combine density functional theory, thermodynamics, and automated Python-based workflows to generate physically grounded datasets describing oxidation states, defect formation, and surface reactivity under realistic conditions. A central aspect of the role is the derivation of interpretable descriptors from electronic structure calculations and the application of machine-learning methods (e.g., Random Forest Trees and related ensemble models) to identify key controls governing catalytic performance.

You will work closely with experimental collaborators to interpret spectroscopic and catalytic measurements and to guide future experiments. In addition, this position will contribute to the development of reusable computational workflows, data products, and analysis approaches that support broader data-enabled research activities at the Center for Functional Nanomaterials, including potential integration with user-facing data services and facility-scale analysis pipelines.

• perform first-principles electronic structure and surface thermodynamics calculations to model redox processes, adsorption, and defect stability in catalytic materials.
• develop and use Python-based, automated computational workflows for simulation setup, execution on HPC resources, and systematic post-processing of results.
• derive physically interpretable electronic, geometric, and thermodynamic descriptors from simulation data and apply machine-learning methods (e.g., Random Forest Trees and related approaches) to identify governing trends.
• use computational spectroscopy and electronic structure analysis to interpret and rationalize experimental measurements, working closely with experimental collaborators.
• contribute to the development of reusable workflows, analysis tools, and data products that support data-enabled research and evolving user-facing data services at the Center for Functional Nanomaterials.

• have a Ph.D. in a relevant discipline (Materials Science, Physics, Electrical Engineering, or a related engineering discipline), conferred within the past five years or to be completed prior to the starting date.
• have experience modeling chemically non-trivial electronic structure, such as mixed or non-integer oxidation states, redox-active materials, defect states, or unconventional bonding environments, using first-principles methods.
• have experience using Python for scientific computing, including data analysis, automation, or workflow development.
• have experience applying machine-learning or statistical methods (e.g., Random Forest Trees, gradient boosting, or related approaches) to analyze scientific datasets.
• have experience working in a high-performance computing (HPC) environment, including job submission and management of computational workloads.
• are committed to fostering an environment of safe scientific work practices.

• have experience deriving and interpreting physically meaningful descriptors from simulation data to rationalize structure–property or structure–reactivity relationships.
• have experience with computational spectroscopy, using electronic structure calculations to interpret or rationalize experimental spectroscopic measurements (e.g., vibrational, electronic, magnetic, or core-level spectroscopy).
• have experience applying LLM-based tools for literature-informed data extraction within computational workflows.
• have familiarity with modeling solvent effects and environmental conditions, such as implicit solvation, temperature, or gas-phase chemical potentials, in computational studies.
• work effectively in a collaborative, interdisciplinary research environment and…